{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## International football results from 1872 to 2018\n",
    "\n",
    "An up-to-date dataset of nearly 40,000 international football results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
       "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
       "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  \n",
       "3   England    False  \n",
       "4  Scotland    False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('results.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['tournament'].str.contains('FIFA', regex=True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取所有的世界杯数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
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       "      <th>1277</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>France</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
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       "      <th>1279</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  home_team   away_team  home_score  away_score  \\\n",
       "1277  1930-07-13    Belgium         USA           0           3   \n",
       "1278  1930-07-13     France      Mexico           4           1   \n",
       "1279  1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280  1930-07-14       Peru     Romania           1           3   \n",
       "1281  1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  \n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_FIFA_all including \"FIFA World Cup\" and \"FIFA World Cup qualification\"\n",
    "\n",
    "df_FIFA_all = df[df['tournament'].str.contains('FIFA', regex=True)]\n",
    "df_FIFA_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
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       "      <th>1277</th>\n",
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       "      <td>Belgium</td>\n",
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       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
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       "      <td>Brazil</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  home_team   away_team  home_score  away_score  \\\n",
       "1277  1930-07-13    Belgium         USA           0           3   \n",
       "1278  1930-07-13     France      Mexico           4           1   \n",
       "1279  1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280  1930-07-14       Peru     Romania           1           3   \n",
       "1281  1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  \n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA = df_FIFA_all[df_FIFA_all['tournament']=='FIFA World Cup']\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          object\n",
       "home_team     object\n",
       "away_team     object\n",
       "home_score     int64\n",
       "away_score     int64\n",
       "tournament    object\n",
       "city          object\n",
       "country       object\n",
       "neutral         bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date          datetime64[ns]\n",
       "home_team             object\n",
       "away_team             object\n",
       "home_score             int64\n",
       "away_score             int64\n",
       "tournament            object\n",
       "city                  object\n",
       "country               object\n",
       "neutral                 bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA.loc[:,'date'] = pd.to_datetime(df_FIFA.loc[:,'date'])\n",
    "df_FIFA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "      <td>FIFA World Cup</td>\n",
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       "      <th>1280</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
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      ],
      "text/plain": [
       "           date  home_team   away_team  home_score  away_score  \\\n",
       "1277 1930-07-13    Belgium         USA           0           3   \n",
       "1278 1930-07-13     France      Mexico           4           1   \n",
       "1279 1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280 1930-07-14       Peru     Romania           1           3   \n",
       "1281 1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  year  \n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  1930  \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  1930  \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  1930  \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  1930  \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  1930  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA['year'] = df_FIFA['date'].dt.year\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          datetime64[ns]\n",
       "home_team             object\n",
       "away_team             object\n",
       "home_score             int64\n",
       "away_score             int64\n",
       "tournament            object\n",
       "city                  object\n",
       "country               object\n",
       "neutral                 bool\n",
       "year                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
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       "      <th>away_score</th>\n",
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       "      <th>1277</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>Belgium</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
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       "      <td>France</td>\n",
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       "      <td>4</td>\n",
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       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>1930-07-14</td>\n",
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       "      <td>FIFA World Cup</td>\n",
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       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
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       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  home_team   away_team  home_score  away_score  \\\n",
       "1277 1930-07-13    Belgium         USA           0           3   \n",
       "1278 1930-07-13     France      Mexico           4           1   \n",
       "1279 1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280 1930-07-14       Peru     Romania           1           3   \n",
       "1281 1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  year  diff_score  \n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  1930          -3  \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  1930           3  \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  1930          -1  \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  1930          -2  \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  1930           1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA['diff_score'] = df_FIFA['home_score']-df_FIFA['away_score']\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          datetime64[ns]\n",
       "home_team             object\n",
       "away_team             object\n",
       "home_score             int64\n",
       "away_score             int64\n",
       "tournament            object\n",
       "city                  object\n",
       "country               object\n",
       "neutral                 bool\n",
       "year                   int64\n",
       "diff_score             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  home_team   away_team  home_score  away_score  \\\n",
       "1277 1930-07-13    Belgium         USA           0           3   \n",
       "1278 1930-07-13     France      Mexico           4           1   \n",
       "1279 1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280 1930-07-14       Peru     Romania           1           3   \n",
       "1281 1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  year  diff_score win_team  \n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  1930          -3           \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  1930           3           \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  1930          -1           \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  1930          -2           \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  1930           1           "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA['win_team'] = ''\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-1</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  home_team   away_team  home_score  away_score  \\\n",
       "1277 1930-07-13    Belgium         USA           0           3   \n",
       "1278 1930-07-13     France      Mexico           4           1   \n",
       "1279 1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280 1930-07-14       Peru     Romania           1           3   \n",
       "1281 1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  year  diff_score win_team  \n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  1930          -3           \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  1930           3           \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  1930          -1           \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  1930          -2           \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  1930           1           "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_FIFA['diff_score'] = pd.to_numeric(df_FIFA['diff_score'])\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建一个新的列数据，包含获胜队伍的信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <th>home_team</th>\n",
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       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
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       "      <th>1277</th>\n",
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       "      <td>Belgium</td>\n",
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       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-3</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>France</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>3</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-1</td>\n",
       "      <td>Yugoslavia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-2</td>\n",
       "      <td>Romania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  home_team   away_team  home_score  away_score  \\\n",
       "1277 1930-07-13    Belgium         USA           0           3   \n",
       "1278 1930-07-13     France      Mexico           4           1   \n",
       "1279 1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280 1930-07-14       Peru     Romania           1           3   \n",
       "1281 1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  year  diff_score  \\\n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  1930          -3   \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  1930           3   \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  1930          -1   \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  1930          -2   \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  1930           1   \n",
       "\n",
       "        win_team  \n",
       "1277         USA  \n",
       "1278      France  \n",
       "1279  Yugoslavia  \n",
       "1280     Romania  \n",
       "1281   Argentina  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first method to get the winners\n",
    "\n",
    "df_FIFA.loc[df_FIFA['diff_score']> 0, 'win_team'] = df_FIFA.loc[df_FIFA['diff_score']> 0, 'home_team']\n",
    "df_FIFA.loc[df_FIFA['diff_score']< 0, 'win_team'] = df_FIFA.loc[df_FIFA['diff_score']< 0, 'away_team']\n",
    "df_FIFA.loc[df_FIFA['diff_score']== 0, 'win_team'] = 'Draw'\n",
    "\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>year</th>\n",
       "      <th>diff_score</th>\n",
       "      <th>win_team</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-3</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>France</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>3</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-1</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>Yugoslavia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>-2</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Romania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  home_team   away_team  home_score  away_score  \\\n",
       "1277 1930-07-13    Belgium         USA           0           3   \n",
       "1278 1930-07-13     France      Mexico           4           1   \n",
       "1279 1930-07-14     Brazil  Yugoslavia           1           2   \n",
       "1280 1930-07-14       Peru     Romania           1           3   \n",
       "1281 1930-07-15  Argentina      France           1           0   \n",
       "\n",
       "          tournament        city  country  neutral  year  diff_score  \\\n",
       "1277  FIFA World Cup  Montevideo  Uruguay     True  1930          -3   \n",
       "1278  FIFA World Cup  Montevideo  Uruguay     True  1930           3   \n",
       "1279  FIFA World Cup  Montevideo  Uruguay     True  1930          -1   \n",
       "1280  FIFA World Cup  Montevideo  Uruguay     True  1930          -2   \n",
       "1281  FIFA World Cup  Montevideo  Uruguay     True  1930           1   \n",
       "\n",
       "        win_team      winner  \n",
       "1277         USA         USA  \n",
       "1278      France      France  \n",
       "1279  Yugoslavia  Yugoslavia  \n",
       "1280     Romania     Romania  \n",
       "1281   Argentina   Argentina  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The second method to get the winners\n",
    "\n",
    "def find_win_team(df):\n",
    "    winners = []\n",
    "    for i, row in df.iterrows():\n",
    "        if row['home_score'] > row['away_score']:\n",
    "            winners.append(row['home_team'])\n",
    "        elif row['home_score'] < row['away_score']:\n",
    "            winners.append(row['away_team'])\n",
    "        else:\n",
    "            winners.append('Draw')\n",
    "    return winners\n",
    "\n",
    "df_FIFA['winner'] = find_win_team(df_FIFA)\n",
    "df_FIFA.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取世界杯所有比赛获胜场数量最多的前20强数据\n",
    "\n",
    "不含预选赛"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "win_team\n",
       "Draw                  186\n",
       "Brazil                 70\n",
       "Germany                66\n",
       "Italy                  45\n",
       "Argentina              42\n",
       "Spain                  29\n",
       "France                 28\n",
       "Netherlands            27\n",
       "England                26\n",
       "Uruguay                20\n",
       "Russia                 17\n",
       "Sweden                 16\n",
       "Hungary                15\n",
       "Poland                 15\n",
       "Belgium                14\n",
       "Mexico                 14\n",
       "Yugoslavia             14\n",
       "Portugal               13\n",
       "Austria                12\n",
       "Switzerland            11\n",
       "Czechoslovakia         11\n",
       "Chile                  11\n",
       "Romania                 8\n",
       "USA                     8\n",
       "Denmark                 8\n",
       "Colombia                7\n",
       "Paraguay                7\n",
       "Croatia                 7\n",
       "Nigeria                 5\n",
       "Korea Republic          5\n",
       "                     ... \n",
       "Cameroon                4\n",
       "Ghana                   4\n",
       "Japan                   4\n",
       "Scotland                4\n",
       "Peru                    4\n",
       "Algeria                 3\n",
       "Northern Ireland        3\n",
       "Serbia                  3\n",
       "Bulgaria                3\n",
       "Ivory Coast             3\n",
       "Australia               2\n",
       "Morocco                 2\n",
       "German DR               2\n",
       "Ukraine                 2\n",
       "South Africa            2\n",
       "Ireland                 2\n",
       "Saudi Arabia            2\n",
       "Senegal                 2\n",
       "Greece                  2\n",
       "Norway                  2\n",
       "Tunisia                 1\n",
       "Slovenia                1\n",
       "Bosnia-Herzegovina      1\n",
       "Iran                    1\n",
       "Slovakia                1\n",
       "Czech Republic          1\n",
       "Korea DPR               1\n",
       "Wales                   1\n",
       "Jamaica                 1\n",
       "Cuba                    1\n",
       "Name: win_team, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df_FIFA.groupby('win_team')['win_team'].count()\n",
    "s.sort_values(ascending=False, inplace=True)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "win_team\n",
       "Brazil                70\n",
       "Germany               66\n",
       "Italy                 45\n",
       "Argentina             42\n",
       "Spain                 29\n",
       "France                28\n",
       "Netherlands           27\n",
       "England               26\n",
       "Uruguay               20\n",
       "Russia                17\n",
       "Sweden                16\n",
       "Hungary               15\n",
       "Poland                15\n",
       "Belgium               14\n",
       "Mexico                14\n",
       "Yugoslavia            14\n",
       "Portugal              13\n",
       "Austria               12\n",
       "Switzerland           11\n",
       "Czechoslovakia        11\n",
       "Chile                 11\n",
       "Romania                8\n",
       "USA                    8\n",
       "Denmark                8\n",
       "Colombia               7\n",
       "Paraguay               7\n",
       "Croatia                7\n",
       "Nigeria                5\n",
       "Korea Republic         5\n",
       "Turkey                 5\n",
       "                      ..\n",
       "Cameroon               4\n",
       "Ghana                  4\n",
       "Japan                  4\n",
       "Scotland               4\n",
       "Peru                   4\n",
       "Algeria                3\n",
       "Northern Ireland       3\n",
       "Serbia                 3\n",
       "Bulgaria               3\n",
       "Ivory Coast            3\n",
       "Australia              2\n",
       "Morocco                2\n",
       "German DR              2\n",
       "Ukraine                2\n",
       "South Africa           2\n",
       "Ireland                2\n",
       "Saudi Arabia           2\n",
       "Senegal                2\n",
       "Greece                 2\n",
       "Norway                 2\n",
       "Tunisia                1\n",
       "Slovenia               1\n",
       "Bosnia-Herzegovina     1\n",
       "Iran                   1\n",
       "Slovakia               1\n",
       "Czech Republic         1\n",
       "Korea DPR              1\n",
       "Wales                  1\n",
       "Jamaica                1\n",
       "Cuba                   1\n",
       "Name: win_team, Length: 62, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.drop(labels=['Draw'], inplace=True)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "win_team\n",
       "Brazil       70\n",
       "Germany      66\n",
       "Italy        45\n",
       "Argentina    42\n",
       "Spain        29\n",
       "Name: win_team, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb4400f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb84db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.head(20).plot(kind='bar', figsize=(10,6), title='Top 20 Winners of World Cup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.sort_values(ascending=True,inplace=True)\n",
    "# s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb6bd128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WNOF7SNOw+m7A8ZKOAv5O0fv5b8B5FMPtd6e78f9OMRd0uL5JMWT/WYpCuOkwgekR8VQzO/u5Zc0bqyGJicL5Ksf5Ksf5Ksf5Ksf5am1ZnntKYtUkXQ4cHxFXN7F73t3dPdohTRj+P6hynK9ynK9ynK9ynK9ynK9yxngOaTbUfhO+h3Q8k7QCcDtwb5PFqJmZmdmE44K0QhHxNPDWquMwMzMzq1I73NRkZmZmZuOYC1IzMzMzq5QLUjMzMzOrlAtSMzMzM6uUb2pqQbue9WDVIZiZTRiX7Lt21SGYtT33kC4hSfPSz2mS9mli/2mSfjP6kZmZmZm1BhekI2caxes/zczMzKwED9mPnOOAdSTNBk4HLgLOBJZP2w+LiJvrD5B0A/CpiJidlm8CDo2I+8YubDMzM7NquSAdOV8AjoyInQEkvQp4V0S8IGkt4GfAzIZjTgYOAD4j6a3Acv0Vo5JmAbMAImL0rsDMrA3VarWqQ+hXR0fHuI1tPHK+yhlv+XJBOnqWAb4naTrQS/9vZDoP+IqkzwMHAaf111BEdAFdaTEf+VDNzNrXeH3/ud/NXo7zVc4Yv8t+SJ5DOnqOAB4HNqLoGV22cYeIeB74JbArIODssQzQzMzMbDxwQTpy/gm8um55KvBoRCwCPgosPcBxJwMnAndExJOjG6KZmZnZ+OMh+5FzH7BQ0r0UQ+/fBy6QtBdwDfBcfwdFxF2SngVObfZEfmZe8zyEU47zVY7zVY7zZWYDcUG6hCJiSvr5ErBDw+YN677/e9pvLrB+30pJnRQ91VeNaqBmZmZm45SH7CskaT/gNuBLaWjfzMzMrO24h7RCEXEGcEbVcZiZmZlVyT2kZmZmZlYpF6RmZmZmVikXpGZmZmZWKRekZmZmZlYpF6RmZmZmVqkJe5e9pN2BC4F1IuLBUT7XNOAdEXF2Wp4J7BcRh4/G+XoP3mU0mp2QHq86gBbjfJXjfJWzJPla+seXjlgcZjb+TOQe0g8DNwIfatwgaaDXeA7XNGCfvoWIuHO0ilEzMzOziWZC9pBKmgJsCWwHXAocI2lb4GjgUWA6sK6krwD7An8FeoC7IuJ/JL0ZOAlYCXgeODgiHpR0GvAsMBN4PXBURJwPHAesI2k2cDpwD3BkROws6RjgjcCa6ecJEXFiivNiYHVgEvCdiOga1cSYmZmZjUMTtYd0N+DKiPg98KSkTdL6zSjeirRuGlbfE9gY2IOiyOzTBXwqImYAR1K8l77PqsBWwM4UhSjAF4AbImJ6RBzfTzxrA+9O5z9a0jJp/UHpHDOBwyWtuERXbWZmZtaCJmQPKcVw/Qnp+zlp+f+A2yNiTlq/FXBJRMwHkHRZ+jkFeAdwnqS+9para/vi9JrP30papcl4/i8iFgALJD0BrAL8jaII3T3tszqwFvCPxoMlzQJmAUREk6c0M5s4arVa1SGMuY6Ojra87uFyvsoZb/macAVp6mXcHlhfUg4sDeTAz4Hn6nbNBmhiKeDpiJg+wPYFTbQx2DG9QEeaQrAjsEVEPC/pWoqh+1dIQ/l9w/l5k+c0M5swenp6qg5hzNVqtba87uFyvsoZq3x1dnY2td9EHLL/IHBGRKwREdMiYnVgDkWPaL0bgQ9ImpR6Rd8PEBHPAnMk7QUgKZO00RDn/Cfw6pJxTgWeSsXo2sDmJY83MzMzmxAmYkH6YeCihnUXUHcXPEBE3EFxw9O9FI+HuhN4Jm3eF/iYpHuBB4BdhzjnfcBCSfdKOqLJOK+k6Cm9D/g6cGuTx5mZmZlNKFmet+8IsKQpETFP0quA64FZEXF31XENIe/u7q46hpbhIZxynK9ynK9ynK9ynK9ynK9yxnjIfsgpjhNuDmlJXZLWpZi7eXoLFKNmZmZmE05bF6QRsc/Qe5mZmZnZaJqIc0jNzMzMrIW4IDUzMzOzSrkgNTMzM7NKuSA1MzMzs0q5IDUzMzOzSrX1XfatqvfgXaoOoWU8XnUALcb5ermlf3xp1SGYmbUFF6QlSPoSxRufeoFFwCci4raSbewCrBsRx41CiGZmZmYtxwVpkyRtAewMbBIRCyTVgGXLthMRl1K8stTMzMzMcEFaxqpAT0QsAIiIHgBJc4Fzge3SfvtExMOSPgB8maJo/Qewb0Q8LukAYGZEHCbpNOBZYCbweuCoiDh/7C7JzMzMrHouSJt3FfBVSb8HfgWcGxHXpW3PRsRmkvYDTqDoSb0R2DwickkfB44CPtdPu6sCWwFrU/ScvqIglTQLmAUQESN7VWY2oFqtNuj2jo6OIfexxZyvcpyvcpyvcsZbvlyQNiki5kmaAbyTojf0XElfSJt/Vvfz+PT9DWmfVSl6SecM0PTFEbEI+K2kVQY4dxfQlRbzJbsSM2tWT0/PoNtrtdqQ+9hizlc5zlc5zlc5Y5Wvzs7OpvbzY59KiIjeiLg2Io4GDgP2TJvqi8S+798FvhcRGwCfACYN0OyCuu/ZSMZrZmZm1gpckDZJ0tskrVW3ajrw5/R977qft6TvU4FH0vf9Rz9CMzMzs9bkIfvmTQG+K2kFYCHwMMW8zp2B5STdRlHgfzjtfwxwnqRHgFuBN41UIH42YvM8hFOO82VmZlXI8txTEpdEust+Zt9d92Mg7+7uHqNTtT4XWOU4X+U4X+U4X+U4X+U4X+WM8RzSIackesjezMzMzCrlIfslFBHTqo7BzMzMrJW5h9TMzMzMKuWC1MzMzMwq5YLUzMzMzCrlgtTMzMzMKuWbmlpQ5+z7qw6hpTT30jLr02756p6+QdUhmJm1PRekDST1AvUV324RMbeicMzMzMwmPBekrzQ/IqYPtFFSR0QsHMuAzMzMzCYyF6RNkHQA8H5gErC8pF2AS4DXAssAX46ISyRNA64AbgTeQfEu+10jYr6ktwA/BFYCeoG9IuKPkj4PCFgOuCgijh7TizMzMzOrmAvSV5osaXb6Picidk/ftwA2jIgnJXUAu0fEs5JqwK2S+l4wvxbw4Yg4WFIAewI/Bc4CjouIiyRNApaStFPafzOK12pdKmnriLi+PiBJs4BZABExahdu1o5qtdqwj+3o6Fii49uN81WO81WO81XOeMuXC9JXGmjI/pcR8WT6ngHfkLQ1sAhYDVglbZsTEX0F7V3ANEmvBlaLiIsAIuIFgFSQ7gTck/afQlGgvqwgjYguoCst5kt4fWZWZ0ne5ex3Z5fjfJXjfJXjfJUzxu+yH5IL0uY9V/d9X4qh9xkR8ZKkuRTD+QAL6vbrBSZTFLD9yYBjI+JHIxyrmZmZWcvwc0iHZyrwRCpGtwPWGGzniHgW+Juk3QAkLSfpVcAvgIMkTUnrV5O08ijHbmZmZjauuId0eM4CLpN0JzAbeLCJYz4K/EjS14CXKG5qukrSOsAtkgDmAR8BnhisIT83sXkewinH+TIzsypkee4piS0m7+7urjqGluECqxznqxznqxznqxznqxznq5wxnkM60NTFf/GQvZmZmZlVygWpmZmZmVXKBamZmZmZVcoFqZmZmZlVygWpmZmZmVXKBamZmZmZVcoFqZmZmZlVqiUejC8pB74dEZ9Ly0cCUyLimEGO2RZ4MSJuTsunAZdHxPlLEMdcYGZELPGDuyTNi4gpwzn23Ac+uqSnN2sbe693ZtUhmJnZEFqlh3QBsIekWoljtgXeMRInl5RJapVcmZmZmbWUlughBRYCXcARwJfqN0haCfgh8Ma06jPAI8AhQK+kjwCfStu2lvRZ4PXAUX29pZI+DwhYDrgoIo6WNA24ArgG2ALYreG8FwOrA5OA70REV1o/D/gOsDMwH9g1Ih6X9CbgbIqcX1nXzqrAucBr0rZDI+KGYWfKzMzMrMW0Uq/fScC+kqY2rP8OcHxEbArsCZwcEXMpitTjI2J6XYG3KrAVRbF4HICknYC1gM2A6cAMSVun/d8GnBERG0fEnxvOe1BEzABmAodLWjGtXx64NSI2Aq4HDq6L8wcpzsfq2tkH+EVETAc2AmaXTYyZmZlZK2uVHlIi4llJZwCHU/Q89tkRWFdS3/JrJL16gGYujohFwG8lrZLW7ZQ+96TlKRQF6l+AP0fErQO0dbik3dP31dMx/wBeBC5P6+8C3pW+b0lRMAOcCfx3+n4HcIqkZVJ8ryhIJc0CZqU8DBCOmfWnVisz06ecjo6OUW1/onG+ynG+ynG+yhlv+WqZgjQ5AbgbOLVu3VLAFhFRX6RSV6DWW1D3Pav7eWxE/Kjh+GnAc/01km6Y2jGd93lJ11IM3QO8FBF5+t7Ly3Oc0yAirk89su8HzpT0rYg4o2GfLoopC/22YWYD6+lZ4nsQB1Sr1Ua1/YnG+SrH+SrH+SpnrPLV2dnZ1H6tNGRPRDwJBPCxutVXAYf1LUianr7+Exiop7TeL4CDJE1Jx68maeUhjpkKPJWK0bWBzZs4z03Ah9L3feviXQN4IiJ+DPwE2KSJtszMzMwmjFbrIQX4X+oKUIoh/JMk3UdxPddT3NB0GXC+pF1ZfFPTK0TEVZLWAW5JvarzgI9Q9G4O5ErgkHTOh4CBhvXrfRo4W9KngQvq1m8LfF7SS+nc+zXRlpmZmdmEkeW5R4BbTN7d3V11DC3DQzjlOF/lOF/lOF/lOF/lOF/ljPGQfTbUfi01ZG9mZmZmE48LUjMzMzOrlAtSMzMzM6uUC1IzMzMzq5QLUjMzMzOrlAtSMzMzM6uUC1IzMzMzq1QrPhi/7a388L9XHULreBiGeu2W1WnBfD3xlmOrDsHMzJZQWxakknqB++tWnRMRxw2zrXkRMWUEYpoGXB4R6y9pW2ZmZmatpC0LUmB+REwfejczMzMzG23tWpD2S9Jc4HTgA8AywF4R8aCklYCzgRWBO4D3ADMioqfu2CnAJcBr07FfjohLUs/nFcCNwDuAR4BdI2K+pBnAKcDzabuZmZlZ22nXgnSypNl1y8dGxLnpe09EbCLpk8CRwMeBo4FfR8Sxkt4DzOqnzReA3SPiWUk14FZJl6ZtawEfjoiDJQWwJ/BT4FTgUxFxnaRvDRSspFl954yIYV+02URUq9UqO3dHR0el5281zlc5zlc5zlc54y1f7VqQDjZkf2H6eRewR/q+FbA7QERcKempfo7LgG9I2hpYBKwGrJK2zYmIvgL4LmCapKnAChFxXVp/JvDe/gKKiC6gKy3mQ12cWTvp6ekZeqdRUqvVKj1/q3G+ynG+ynG+yhmrfHV2dja1nx/79EoL0s9eFhfsWRPH7QusRDGUPx14HJjU0GZ9uxkuLs3MzMxckDbpRkAAknaimCfaaCrwRES8JGk7YI3BGoyIp4FnJG2VVu07gvGamZmZtYx2HbJvnEN6ZUR8YZD9/wP4maS9geuAR4F/NuxzFnCZpDuB2cCDTcRxIHCKpOeBXzQbvJ+72DwP4ZTjfJmZWRWyPPeo8VAkLQf0RsRCSVsAP6jwsVF5d3d3RaduPS6wynG+ynG+ynG+ynG+ynG+yhnjOaRDTn1s1x7Sst4IhKSlgBeBgyuOx8zMzGzCcEHahIj4A7Bx1XGYmZmZTUS+qcnMzMzMKuWC1MzMzMwq5YLUzMzMzCrlgtTMzMzMKuWbmlrQiSeeWHUINkF97WtfqzoEMzNrQ23VQyppmqTfNKw7RtKRVcVkZmZm1u5K95BKehXwFmBK/fqIuHmkgqqapI6IWFh1HGZmZmbtoFRBKmk/4HsUD4efX7cpp3h4fMuSdC1wM7AlcKmkDYDLI+L8tH1eRExJD8f/HrANMIeil/mUiDhf0lxgZkT0SJoJ/E9EbCtpM+AEYDJF3g6MiIck3QB8KiJmp3PcBBwaEfeN4aWbmZmZVapsD+k3gT0j4pejEcw4sEJEbAMg6bQB9tkDmAZsAKwM/A44ZYh2HwS2Tq8e3RH4BrAncDJwAPAZSW8FluuvGJU0C5gFEBHlrsishI6ODmq1WtVhtAznqxznqxznqxznq5zxlq+yBemLwLWjEMdYyYdYf24TbWwFnBcRi4DHJF3TxDFTgdMlrZXOtUxafx7wFUmfBw4CTuvv4IjoAroaYjUbcQsXLvS7oEvwu7PLcb7Kcb7Kcb7KGeN32Q+p7E1NXwG+LWn8lNTl/AN4bcO61wF9v5Hn6tYvJOVHUgYsm9Zng7T/r2OASXXrvw6dMzzrAAAgAElEQVRcExHrAx/o2xYRzwO/BHYFBJxd4lrMzMzMJoSyBenvgV2AxyX1ps8iSb2jENuIi4h5wKOSdgCQ9DrgPcCN/ew+F5iRvu/K4l7NG4E9JS0laRVg2wGO2bNu/VTgkfT9gIbznAycCNwREU+WuiAzMzOzCaDskP2ZwBkUQ9vzh9h3vNoPOEnS/6bl/4iIP0pq3O/HwCWSbgeuZnHv6QXADsBvKAr024Bn+toCfiLpi2l9n29SDNl/Fvh1/Uki4i5JzwKnNnsBhx9+eLO7tj0P4ZiZmY1/WZ43PyVR0lPA6yKirecxSpoSEfMkrQjcDmwZEY8Ns61Oinm5a6d5qUPJu7u7h3OqtuSCtBznqxznqxznqxznqxznq5wxnkM62HRHoPyQ/anAR4cT0ARzuaTZwA3A15egGN2Poif1S00Wo2ZmZmYTTtkh+82AwyR9CXi8fkNEbD1iUY1zEbHtCLVzBsUUCDMzM7O2VbYg/XH6mJmZmZmNiFIFaUScPlqBmJmZmVl7Gs677FehGLqvUTdJNSKGeluRmZmZmdkrlH2X/W7AT4E/AOsBDwDrUzyb0wWpmZmZmZVWtof0P4EDI+I8SU9FxMaSDqQoTm2MXHbu01WH0EImXq4+sPcKVYdgZmY2osoWpG+MiPMa1p0OPAYcOTIhVS+9eep+ivzMAT4aESNS2UiaCewXEX66vZmZmRnln0P6RJpDCjBX0hbAm4GlRzasys2PiOnp3fNPAv9vpBqOiDtdjJqZmZktNpzHPm1F8frM44FrgEXA/w52UIu7BdgQQNK2wJERsXNa/h5wZ0ScJuk4YBdgIXBVRBwpaS/gaKAXeCYitq5vQ9JmwAnAZIpXsR4YEQ+N7eWZmZmZVatUD2lE/HdEXJC+nwG8FZgREV8ZjeCqJmlpivfWXzrEfq8DdgfWi4gNKebaAnwVeHdEbERRrDZ6ENg6IjZO+35jpGI3MzMzaxXDeezTMsDmQGdEnCtpeUnLR8RzIx9eZSanV4NOA+4CfjnE/s8CLwAnS/o/4PK0/ibgNEkBXNjPcVOB0yWtBeTAMv01LmkWMAsgIspdiU04tVpt1Nru6OgY1fYnGuerHOerHOerHOernPGWr7KPfdqAordwAfAG4FxgG2B/YO8Rj6468yNiuqSpFMXl/wNOpBiOr+9VngQQEQvT8PsOwIeAw4DtI+IQSW8H3g/MljS94TxfB66JiN0lTQOu7S+YiOgCutJiPgLXZy2sp6dn1Nqu1Wqj2v5E43yV43yV43yV43yVM1b56uzsbGq/sjc1/QD4akSsDbyU1l1HMa90womIZ4DDgSNTz/CfgXUlLZeK1R0AJE0BpkbEz4HPANPT+jdHxG0R8VWgB1i94RRTgUfS9wNG+3rMzMzMxqOyBel6FA/Gh9RTl4bqJ49kUONJRNwD3At8KCL+CgRwH3AWcE/a7dXA5ZLuoyjQj0jrvyXpfkm/Aa5P7dT7JnCspJuYeE8qMDMzM2tK2Tmkc4EZwJ19K9JQ9cMjGFPlImJKw/IH6r4fBRzVz2Gb9dPOHv3sd236EBG3UNwY1qepm8P8YPTmeQjHzMxs/CtbkH4F+D9JPwSWlfTvwCHAwSMemZmZmZm1hbKPfboceC+wEsXQ9BrAHhFx1SjEZmZmZmZtoOxd9nulV4d+smH9ByPi/BGNzMzMzMzaQtmbmn4ywPquAdabmZmZmQ2qqR5SSWumr0tJehOQ1W1ek+Kh8GZmZmZmpTU7ZP8wxWOeMuCPDdseA44ZwZjMzMzMrI00VZBGxFIAkq6LiG1GNyQzMzMzayelbmpqphiV9GxEvGb4IY0dSV8C9gF6gUXAJyLithFqe17j80xHyq5nPTgazdo4csm+a1cdgpmZ2Zgpe1NTM7Khd6mepC2AnYFNImJDYEfgr9VGZWZmZtZ+yj4Yvxn5KLQ5GlYFeiJiAUBE9EjaTNL3ImIPSbsC51C8b34p4LcRsaakNwMnUTyL9Xng4Ih4MN3sdTZFTq+sP5GkzwMClgMuioijJU0DrgBuBN5B8U77XSNi/qhfuZmZmdk4Mho9pK3iKmB1Sb+X9H1J2wB3Axun7e8EfgNsCrwd6BvK7wI+FREzgCOB76f13wF+EBGbUtzoBYCknYC1KF4tOh2YIWnrtHkt4KSIWA94GthzVK7UzMzMbBwbjR7SlhAR8yTNoCg8twPOBb4APCxpHYoC8tvA1sDSwA2SplD0Zp4nqa+p5dLPLVlcUJ4J/Hf6vlP63JOWp1AUon8B5kTE7LT+LmBaf7FKmgXMSnEP+5qtddRqtUrO29HRUdm5W5HzVY7zVY7zVY7zVc54y9doFKQtMYcUICJ6gWuBayXdD+wP3EDxetSXgF8Bp1EUpEdS9Cg/HRHTB2iyv+kKGXBsRPyofmUasl9Qt6oXmDxAnF0sfvlAq0yJsCXQ09NTyXlrtVpl525Fzlc5zlc5zlc5zlc5Y5Wvzs7OpvYbjSH7945CmyNO0tskrVW3ajrwZ+B64DPALRHxd2BFYG3ggYh4Fpgjaa/URiZpo3T8TcCH0vd969r9BXBQ6l1F0mqSVh6t6zIzMzNrNWXfZf8m4L8oireXPdIoIt6Yft44YtGNrinAdyWtACykePj/LOA5YBWKwhTgPuCJiOjrmdwX+IGkLwPLUNz4dC/waeBsSZ8GLug7SURclaYA3JKG+ecBH6HoETUzMzNre1meNz8CLOkWijc1nUVxh/m/RMR1IxuaDSDv7u6uOoaW4SGccpyvcpyvcpyvcpyvcpyvcsZ4yH7I6Zxl55CuB2wZEYuGE5SZmZmZWaOyc0ivZ/FjkczMzMzMlljZHtK5wC8kXUjdszYBIuKrIxWUmZmZmbWPsgXp8sBlFDfzrD7y4ZiZmZlZuylVkEbEgaMViJmZmZm1pyELUknTImJu+r7mQPtFxJ9GMC4zMzMzaxPN9JDeD7w6fX94gH1yircZmZmZmZmVMmRBGhGvrlvcOCLuHcV4rAm9B+9SdQgt4/GqAxjA0j++tOoQzMzMxo2yNzVdJml5ive9X0fxHvjZdW8xGtckzYuIKXXLBwAzI+Kw6qIyMzMza2+lnkOaXg+6KXAxsCFwPvCUpMtHIbYJTVLZfwyYmZmZTUili6KI+FMqppZNn/cAK490YGNN0mnA5RFxflqeFxFTJG0LHAP0AOsDdwEfiYhc0vuAb6dtdwNrRsTOkjYDTgAmA/OBAyPiodQj+35gErC8pEeA8yPiknTOs4BzI8LjuWZmZtY2ShWkks4BtgQeoRiuPws4JCL+OfKhjYrJkmbXLb8OaKb425jitandwE3AlpLuBH4EbB0RcyT9rG7/B9P6hZJ2BL4B7Jm2bQFsGBFPStoGOAK4RNJU4B3A/o0nlzQLmAUQEc1frY1btVqt6hD61dHRMW5jG4+cr3Kcr3Kcr3Kcr3LGW77K9pDOBHqBe9NndgsVowDzI2J630LfHNImjrs9Iv6WjpkNTAPmAX+KiDlpn5+RikZgKnC6pLUonkCwTF1bv4yIJwEi4jpJJ0laGdgDuCAiFjaePCK6gK602BLzdW1wPT09VYfQr1qtNm5jG4+cr3Kcr3Kcr3Kcr3LGKl+dnZ1N7Vd2DulbgM2BXwNbAVdI+r2kk0tHOP4sJOVDUkYxHaHPgrrvvRSFfDZIW18HromI9YEPUAzR93muYd8zgX2BA4FThxW5mZmZWQsrVZACRMRjwEMUzySdC7weeO/IhlWJucCM9H1XXt6r2Z8HgTUlTUvLe9dtm0oxrQHggCHaOQ34DEBEPNBUpGZmZmYTSNk5pJdS9Iz+k+KxT5cBR0bEH0YhtrH2Y4q5nLcDV/PKnsyXiYj5kj4JXCmpB7i9bvM3KYbsP0vRmzxYO49L+h3Fkwua4mdYNs9DOGZmZuNflufNT0lMcy6vq5s32dYkTYmIeWmI/yTgDxFxfMk2XkXxNqxNIuKZJg7Ju7u7hxFte3JBWo7zVY7zVY7zVY7zVY7zVc4YzyEdbJojULKHNCJOG2Y8E9XBkvanmG96D8Vd901Ld+CfAny7yWLUzMzMbMLxw9mXQOoNLdUj2nD8r4A3jlxEZmZmZq2n9E1NZmZmZmYjyQWpmZmZmVXKBamZmZmZVcoFqZmZmZlVyjc1taDeg3epOoSW8XjVAQzAz5I1MzNbzAVpA0m9FM8F7QB+B+wfEc8Psv+8iJgyAuedBlyeXjdqZmZm1jY8ZP9K8yNieioMXwQOqTogMzMzs4nMPaSDuwHYECC9BvSgtP7kiDihfkdJU4BLgNcCywBfjohLUs/nFcCNwDso3nG/a3r16AyKB+M/n7abmZmZtR0XpAOQ1AG8l+Jd9TOAA4G3U7z+6jZJ10XEPXWHvADsHhHPSqoBt0rqmyi4FvDhiDhYUgB7Aj8FTgU+FRHXSfrWILHMAmYBRMTIXqhVolarVR1Cvzo6OsZtbOOR81WO81WO81WO81XOeMuXC9JXmixpdvp+A/AT4FDgooh4DkDShcA7KV4X2icDviFpa2ARsBqwSto2JyL62rwLmCZpKrBCRFyX1p9JUQC/QkR0AV1pMV/C67NxYLy+b9nvgi7H+SrH+SrH+SrH+SpnjN9lPyQXpK80PyKm16+QlDVx3L7ASsCMiHhJ0lxgUtq2oG6/XmAyRQHr4tLMzMzanm9qas71wG6SXiVpeWB3it7TelOBJ1Ixuh2wxmANRsTTwDOStkqr9h3poM3MzMxagQvSJkTE3cBpwO3AbRQ3Nd3TsNtZwExJd1IUlw820fSBwEmSbgHmj1zEZmZmZq0jy3OPGreYvLu7u+oYWobnFJXjfJXjfJXjfJXjfJXjfJUzxnNIh5z66B5SMzMzM6uUC1IzMzMzq5QLUjMzMzOrlAtSMzMzM6uUC1IzMzMzq5QLUjMzMzOrlAtSMzMzM6tUS786NL3S8wbgvyLiirROwEER8Z5RPG8H0BMRKwzj2NWB/4mIvYd7/s7Z9w/30LbU3Ft0R1b39A0qOKuZmVlraumCNCJySYcA50m6Blga+C9g1IrRJRURfwWGXYyamZmZTTQtXZACRMRvJF0G/BuwPHAGkEmaHRHTASR9AeiIiP+UtDnQBcwDbgLeFRHTJU0GfghsArwEfCYirpe0AXAKsAzFFIfdgL/0nV/Sa4CLgRUo8vnFiLhc0v8CD0VEV9rvP4G/A/8HnJ/O+WaKV5JOARYBn4yI20YtWWZmZmbj0ESZQ/ofwD7Ae4FvDrHvqcDHI+IdvPxVVocDL0bEBsBHgTMlLQt8kmKIfTqwKdD43s75wK4RsQmwI3B8Wn8OL+8J3Qs4r+HYRykK4o2BfYETh7pQMzMzs4mm5XtIASLiOUnnAvMiYkExjfSVJNWAZSPi9rTqbIoiEmAr4FupvQckdQNvAW4GvixpDeDCiHg4zSHtkwH/LWkril7O1SXVIuIOSatLWgV4A/BYRHRLekvdscsB35O0EbAQePMAcc8CZqXYyqTGKlKr1aoOYVg6OjpaNvYqOF/lOF/lOF/lOF/ljLd8TYiCNFmUPlAUd/W9v5PSuqzxoDr9bouIMyXdArwf+KWk/SmK1D77AVOBTSJioaS/pfMBXADsCUyj6DFt9Dngr8BHKKYEzBsghi6KaQYA+SDXYONET09P1SEMS61Wa9nYq+B8leN8leN8leN8lTNW+ersbO7W4okyZN/oMaBT0mslTaIoJomIvwMvSZqZ9vtQ3THXUwybI2kdYFXgYUlrRsTDEfEdivmfGzacayrwRCpG3wWsVrftnHSOPSiK00ZTgUcjIgf2Z/CC2czMzGxCmpAFaUS8AHwDuAO4FPht3eaDgFMl3UzRo/pMWv9dYLKk+4GzgP0i4kVgH0kPSJoNrAn8tOF0ZwLvkHQnxTzRP9TFcS+wEjAnIp7oJ9TvAR+XdCuwBrBgCS7bzMzMrCVled5eI8CSpkTEvPT9S8DrIuJzFYdVRt7d3XhflQ3EQzjlOF/lOF/lOF/lOF/lOF/ljPGQ/ZAjwBNpDmmzdpF0FMW1zwUOqDQaMzMzszbXdgVpRJxNcXe9mZmZmY0DE3IOqZmZmZm1DhekZmZmZlYpF6RmZmZmVikXpGZmZmZWKRekZmZmZlaptrvLfiI494GPVh2CDWHv9c6sOgQzM7OW4YK0jqQc+GlEfDQtdwCPArdFxM7DaG8XYN2IOG5kIzUzMzObOFyQvtxzwPqSJkfEfOBdwCPDbSwiLqV4damZmZmZDcAF6StdAbwfOB/4MPAz4J0AkpaneOf9BhS5OyYiLpH0WWD9iDhI0gbpmM0AATMj4jBJqwA/BNZM5zk0Im5Oxx6U1p0cESeMyVWamZmZjRMuSF/pHOCrki4HNgROIRWkwJeAX6fCcwXgdkm/Ak4ArpW0e9rnExHxvKT6dk8ErouI3SUtDUyRNAM4EHg7xXteb5N0XUTcU3+gpFnALICIGJ2rthFVq9WqDmFYOjo6Wjb2Kjhf5Thf5Thf5Thf5Yy3fLkgbRAR90maRtE7+vOGzTsBu0g6Mi1PAt4YEb+TdABwH/CjiLipn6a3B/ZL5+gFnpG0FXBRRDwHIOlCiuL3ZQVpRHQBXWkxX7IrtLHQ09NTdQjDUqvVWjb2Kjhf5Thf5Thf5Thf5YxVvjo7O5vazwVp/y4F/gfYFlixbn0G7BkRD/VzzFrAPKC5zC9uz8zMzKyt+Tmk/TsF+FpE3N+w/hfApyRlAJI2Tj+nAt8BtgZWlPTBftq8Gjg07b+0pNcA1wO7SXpVmp+6O3DDaFyQmZmZ2XjlHtJ+RMTfKArMRl+nmC96XypK5wI7A8cD34+I30v6GHCNpOsbjv000JW291Lc1HSLpNOA29M+JzfOH+2Pn3HZPA/hmJmZjX9ZnntKYovJu7u7q46hZbggLcf5Ksf5Ksf5Ksf5Ksf5KmeM55AOOUXRQ/ZmZmZmVikXpGZmZmZWKRekZmZmZlYpF6RmZmZmVikXpGZmZmZWKRekZmZmZlYpP4e0Ba388L9XHULreBhWruC0T7zl2ArOamZm1prasiCV1AvcT/FcrF7gsIi4eYhj5kXElCH2ORn4dkT8dsSCNTMzM5vg2rIgBeZHxHQASe8GjgW2WdJGI+LjS9qGmZmZWbtp14K03muAp/oWJH0eELAccFFEHF2/s6SlgO9RFLBzKObhnhIR50u6FjgyIu6s71FN77bfOSIOSK8KnQ+sDawBHAjsD2wB3BYRB4zitZqZmZmNO+1akE6WNBuYBKwKbA8gaSdgLWAziuH8SyVtHRH176XfA5gGbEAxPfF3wCklz//adM5dgMuALYGPA3dImh4Rs+t3ljQLmAUQESVPZVWo1WpVhzAsHR0dLRt7FZyvcpyvcpyvcpyvcsZbvtq1IK0fst8COEPS+sBO6XNP2m8KRYFaX5BuBZwXEYuAxyRdM4zzXxYRuaT7gccj4v4UywMUxe7LCtKI6AK60mI+jPPZGGvV9yn7XdDlOF/lOF/lOF/lOF/ljPG77IfUrgXpv0TELZJqwEoUvaLHRsSPBjkka7Lp+sJxUsO2Bennorrvfctt/zsxMzOz9tL2zyGVtDawNPAP4BfAQZL65n6uJqnxqUE3AntKWkrSKsC2AzT9uKR10pzT3UcnejMzM7PW1669cX1zSKHo8dw/InqBqyStA9wiCWAe8BHgibpjLwB2AH4D/B64DXimn3N8Abgc+Gvad9BHRpXhZ1w2z0M4ZmZm41+W556SWJakKRExT9KKwO3AlhHx2BidPu/u7h6jU7U+F6TlOF/lOF/lOF/lOF/lOF/ljPEc0iGnO7ZrD+mSulzSCsCywNfHsBg1MzMzm3BckA5DRGxbdQxmZmZmE0Xb39RkZmZmZtVyQWpmZmZmlXJBamZmZmaVckFqZmZmZpVyQWpmZmZmlWq7u+wl9QL3U1z77ygeiv98ieO/GBHfGIW4pgGXR8T6Q+174oknjvTpbRgOP/zwqkMwMzObENqxh3R+RExPhd+LwCHNHCQpS68B/eKoRmdmZmbWZtquh7TBDcCGAJI+CxyU1p8cESekXssrgGuALYDZLH7t6APAl6jr1ZR0JDAlIo6RtCnwE+A54EbgvRGxfmrzTGD5dK7DIuLmUb9SMzMzs3GqHXtIAZDUAbwXuF/SDOBA4O3A5sDBkjZOu74NOCMiNo6IA1ncw7rvEKc4FTgkIrYAeuvWPwG8KyI2AfYGPP5uZmZmba0de0j7ejih6CH9CXAocFFEPAcg6ULgncClwJ8j4tYyJ0ivFX11Xc/n2cDO6fsywPckTacoVN/aRHuzgFkAEVEmFBtFtVqt6hBGXEdHx4S8rtHifJXjfJXjfJXjfJUz3vLVjgXp/IiYXr9CUjbI/s8Nsm0hL+9lnpR+DtbeEcDjwEbp2BcG2ReAiOgCutJiPtT+NjZ6enqqDmHE1Wq1CXldo8X5Ksf5Ksf5Ksf5Kmes8tXZ2dnUfm07ZN/gemA3Sa+StDywO0XvaX9ekrRM+v44sLKkFSUtR+oFjYingH9K2jzt96G646cCj0bEIuCjwNIjfC1mZmZmLcUFKRARdwOnAbcDt1Hc1HTPALt3AfdJOisiXgK+lo65HHiwbr+PAV2SbqHoMX0mrf8+sL+kWymG6wfrgTUzMzOb8LI89wjwaJA0JSLmpe9fAFaNiE+PQNN5d3f3CDTTHjyEU47zVY7zVY7zVY7zVY7zVc4YD9kPNpURaM85pGPl/ZL+nSLHfwYOqDYcMzMzs/HJBekoiYhzgXOrjsPMzMxsvPMcUjMzMzOrlAtSMzMzM6uUC1Kz/9/e3QfZVdd3HH+n2QACKRlYBCJUYIYSaBzCY2GQZ6lgKQTFD6SUAWEIrSBFS1F8gBSpxRlGw4PWCaiBSoFPY5AILYIoBB94RkGiIipU3NSQCiMQjSZs/zi/LZfl3t09CbPn3uTzmtnhnnPP+d3f+SR38+X8fueciIiIaFQK0oiIiIhoVArSiIiIiGhUrrLvQV+98fmmu9BD1jyrvzp+yuvYj4iIiOgkBekwko4FFgK72P7RaNu32X8m8ITtJR3e/1tghe1r166nEREREeuGFKSvNQv4FtXz5+eswf4zqR4j+pqCVFKf7c+tVe8iIiIi1jEpSFtI2hTYHzgEWATMkXQwcK7to8o2VwIP2p4v6RLgaGAVcDvVmdWjgYMkfRR4F/B54Dul3UWSJgMv2r5U0unAbGAD4EngJNsrxu2AIyIiIrpACtJXmwncZvsJSb+WtEenDSVtDhwLTLM9KGmK7eclLQJusb2gbAcwxfZBZXlOSzMLbV9V1l8MnAZc0eazZlMVrth+HQ4zxqK/v7/pLoy7vr6+9fK411Tyqid51ZO86kle9XRbXilIX20WMLe8vqEs39ph298AvwOulnQr1TB9J50eITq9FKJTgE2Br7XbyPY8YF5ZHBzhc+J1tHz58qa7MO76+/vXy+NeU8mrnuRVT/KqJ3nVM155TZ06dUzb5bZPhaQtgEOpCsyngH8EjgdW8+qcNgKwvQrYB/gy5czqCM2/1GH9fOAs228B/mmo7YiIiIj1Sc6QvuI44FrbZwytkHR3ebmrpA2pCsbDgG+V+aYb2/5PSfdSzQEFeAGYPMbPnAwslTQJOBH45etwHBERERE9JQXpK2YBlwxb92XgrwEDjwI/AR4p700Gbpa0ETABeH9ZfwNwlaSzqYrckXwMuA94GniMMRayuT/m2GUIJyIiovtNGBzMlMQeMzgwMNB0H3pGCtJ6klc9yaue5FVP8qonedUzznNIJ4y2XeaQRkRERESjUpBGRERERKNSkEZEREREo1KQRkRERESjUpBGRERERKNSkEZEREREo3If0h50zHU/aroL64ybT5zWdBciIiLWez1XkEr6CNXN6lcDLwNn2L5vlH0uAhbb/rqkc4B5tle8Dn2ZA7xo+9LXoa35wC22F6xtWxERERG9pKcKUkn7AUcBe9heKakf2GC0/Wxf0LJ4DvAlYK0KUkk9lV1EREREt+q1omobYLntlQC2l0vaR9KVtt8p6RiqR3duRjU/dontHYfOPgJTy883JS0HPg1cVNp+A7CB7R0k7Ql8CtgUWA6cYnuppLuA7wD7A4taOybpdGA2VYH8JHCS7RXls38D7AVsDZxne4GkCcAVwKHAzxnDUwwiIiIi1kW9VpDeDlwg6Qng68CNwLeB3cv7BwA/APamOrZXDeXbvlzSB4BDbA89L2sRgCQDd0uaRFUoHmP7WUnHA/8MnFq2n2L7oLLPnJbmF9q+qqy/GDittANVIf1WYFr5vAXAscDOwFuArYAlwBfaHbSk2VTFLrbHklOMUX9/f9Nd6Cp9fX3JpIbkVU/yqid51ZO86um2vHqqILX9Yjl7eQBwCFVB+iHgSUm7APtQndk8EJgI3DOWdiWdB/zW9mckTQemA3dIorSztGXzGzs0M70UolOozqx+reW9r9h+GVgiaauy7kDgeturgQFJ3xjhuOcB88ri4FiOKcYmzz1+tTwLup7kVU/yqid51ZO86hnnZ9mPqqcKUoBSwN0F3CXpMeBkqsLzSOAPVGdO51MVkueO1p6kw4B3UxWIUA2dP257vw67vNRh/Xxgpu3vSzoFOLjlvZUtr1uH5lNcRkRExHqvp+5DKmlnSTu1rJoBPA0sprpY6bu2nwW2oBoef7xNMy8Ak0t7bwY+C8j2b8v7Pwa2LBdQIWmSpD8bQ/cmA0vLkP+JY9h+MXCCpImStqE64xsRERGx3um1M6SbAldImgKsorp4aDbVWcutqIo8gEeBZbbbnYGcB/yXpKVUZ1q3AG4qw/MDtt8h6TjgckmbUWU0l/bFbauPUc1ZfRp4jFL0juAmqguaHgOeAO4eZfv/l3tnjl2GcCIiIrrfhMHBjBr3mMGBgYGm+9AzUpDWk7zqSV71JK96klc9yauecZ5DOuqdhHpqyD4iIiIi1j0pSCMiIiKiUSlIIyIiIqJRKUgjIiIioi2YwIQAAAixSURBVFEpSCMiIiKiUSlIIyIiIqJRKUgjIiIiolHjdmN8SVtT3WB+b6pHaT4FnGP7ibVsdw7wou1L17Kdu4BzbT+4Nu0Ma/NF25sOWzcVuNz2cWva7urTj17rvq0vfjXK+xOvWjQu/YiIiIjOxqUglTSB6slE19g+oaybQfV0pbUqSHuN7QFgjYvRiIiIiHXNeJ0hPQT4g+3PDa2w/T1JF0m6oqzaErjd9nsk/Q1wNrAB1eM432t7taQjgE8AE4Hltg8r++5aznD+CTDX9uUAkj4AnFq2udr2XEmbAAa2Le183PaNrZ2VNAv4MNWTBW61/UFJfwfsYPu8ss0pwJ623yfpK8B2wEbAZbbnDWuvH/gqcDHVI0hvsT1d0vbAvwGblE3Psv2dmtlGRERE9LTxmkM6HXho+ErbF9ieARwE/C9wpaRdgOOB/ct7q4ETJW0JXAW8y/ZuwLtbmpoGvB3YB7hQ0iRJewLvAf4c2Bc4XdLuwBFUz6zfzfZ04LbWPpUh9U9SPWd+BrC3pJnAAuCdLZseDwwVsqfa3hPYCzhb0hYt7W0F3ApcYPvWYREsAw63vUdp7/IRU4yIiIhYB43bHNJOynD+dcCnbT8k6SxgT+ABSQBvoCrc9gUW2/45gO1ftzRzq+2VwEpJy6imArwVuMn2S+VzFgIHUBWgl0r6JNWZynuGdWlv4C7bz5b9rgMOtP0VST+TtC/wE2Bn4Ntln7MlHVtebwfsRFVgTwLuBM60fXebw59EVYQPFd5/2iGj2cDsctydoow10N/f33QXukpfX18yqSF51ZO86kle9SSverotr/EqSB+n87zJOcAztr9YlidQzTU9v3UjSUcDgx3aWNnyejXVcU1ot6HtJ8rZ03cA/yLpdtsXtWzSdr/iRkDAj6iK3UFJBwNvA/azvaJMHdiobL+K6szw24F2Ben7qa672Y3qbPXvOvR5HjA0DaBTBrEGli9f3nQXukp/f38yqSF51ZO86kle9SSvesYrr6lTp45pu/Easv8GsKGk04dWSNpb0oXA4VTzRYfcCRwn6Y1lu80lvRn4LnCQpB2G1o/ymYuBmZI2LvNGjwXuKUPyK2x/CbgU2GPYfveVz+mXNBGYxSvF5EJgZlk3NFy/GfBcKUanUZ3JHTJINYd1mqQPtenjZsBS2y8DJ1HNaY2IiIhYr4xLQWp7kKogPFzSTyU9TnVm9GBgKnC/pO9Jusj2EuCjwO2SHgXuALYpQ+izgYWSvs8rBWGnz3wYmA/cT1VkXm37EeAtQ58HfITqQqPW/ZYC5wPfBL4PPGz75vLec8AS4M227y+73Ab0lb5+HLh3WHurgROAQyS9d1g3PwucLOlequH6l0Y6poiIiIh10YTBwYwA95jBgYGBpvvQMzKEU0/yqid51ZO86kle9SSvesZ5yH6k6ZBAntQUEREREQ1LQRoRERERjUpBGhERERGNyhzS3pM/sIiIiOglmUO6rpH0ENUfbH7G8JO8klfy6p6f5JW8klf3/IxzXqNKQRoRERERjUpBGhERERGNSkHae+aNvkm0SF71JK96klc9yaue5FVP8qqnq/LKRU0RERER0aicIY2IiIiIRvU13YEYO0lHAJcBE4GrbV/ScJe6iqQvAEcBy2xPL+s2B24EtgeeAmT7uab62C0kbQdcC2wNvAzMs31Z8mpP0kbAYmBDqt+bC2xfKGkH4AZgc+Bh4CTbv2+up91F0kTgQeCXto9KXp1Jegp4AVgNrLK9V76PnUmaAlwNTKe6HeKpwI9JXq8haWeqXIbsCFxA9W9A1+SVM6Q9ovxi/wxwJLArMEvSrs32quvMB44Ytu5DwJ22dwLuLMsBq4B/sL0LsC9wZvn7lLzaWwkcans3YAZwhKR9gU8Cny55PQec1mAfu9HfAz9sWU5eIzvE9gzbe5XlfB87uwy4zfY0YDeqv2fJqw3bPy5/r2YAewIrgJvosrxSkPaOfYAnbf+snFG4ATim4T51FduLgV8PW30McE15fQ0wc1w71aVsL7X9cHn9AtUv8zeRvNqyPWj7xbI4qfwMAocCC8r65NVC0rbAX1KdxULSBJJXXfk+tiHpj4EDgc8D2P697edJXmNxGPBT20/TZXmlIO0dbwJ+0bL8TFkXI9vK9lKoijDgjQ33p+tI2h7YHbiP5NWRpImSvgcsA+4Afgo8b3tV2STfyVebC5xHNSUEYAuS10gGgdslPSRpdlmX72N7OwLPAl+U9IikqyVtQvIaixOA68vrrsorBWnvaPekg9wiIdaKpE2BLwPn2P5N0/3pZrZXlyGvbalGLHZps1m+k4CkobncD7Wszu+wke1vew+qaVlnSjqw6Q51sT5gD+Bfbe8OvESG50claQPgaOA/mu5LOylIe8czwHYty9sCAw31pZf8StI2AOW/yxruT9eQNImqGL3O9sKyOnmNogwN3kU193aKpKGLQ/OdfMX+wNHlQp0bqIbq55K8OrI9UP67jGp+3z7k+9jJM8Aztu8rywuoCtTkNbIjgYdt/6osd1VeKUh7xwPATpJ2KP+XcwKwqOE+9YJFwMnl9cnAzQ32pWuU+XyfB35o+1MtbyWvNiRtWa7qRdIbgLdRzbv9JnBc2Sx5FbbPt72t7e2pfld9w/aJJK+2JG0iafLQa+AvgB+Q72Nbtv8H+EW5ehyqeZFLSF6jmcUrw/XQZXnltk89wvYqSWcBX6O67dMXbD/ecLe6iqTrgYOBfknPABcClwCWdBrw38C7m+thV9kfOAl4rMyLBPgwyauTbYBryt0u/giw7VskLQFukHQx8AjlIovo6IMkr3a2Am6SBNW/y/9u+zZJD5DvYyfvA64rJ2h+BryH8t1MXq8laWPgcOCMltVd9fs+T2qKiIiIiEZlyD4iIiIiGpWCNCIiIiIalYI0IiIiIhqVgjQiIiIiGpWCNCIiIiIalYI0IiIiIhqVgjQiIiIiGpWCNCIiIiIa9X+zS2vOU80KfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7322b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.sort_values(ascending=True,inplace=True)\n",
    "s.tail(20).plot(kind='barh', figsize=(10,6), title='Top 20 Winners of World Cup')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb6c2898>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbe2af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_percentage = s/s.sum()\n",
    "s_percentage\n",
    "s_percentage.tail(20).plot(kind='pie', figsize=(10,10), autopct='%.1f%%', \n",
    "                           startangle=173, title='Top 20 Winners of World Cup', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 来查看部分国家的获胜情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NA'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('China', default = 'NA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "China，呵呵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('Japan', default = 'NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('Korea DPR', default = 'NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('Korea Republic', default = 'NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NA'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('Egypt', default = 'NA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "埃及，本次世界杯的黑马，之前在世界杯上也没有赢过~~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 各个国家队进球总数量情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>France</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>Peru</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>Chile</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>Austria</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>Italy</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35562</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35563</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35564</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35565</th>\n",
       "      <td>Greece</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35566</th>\n",
       "      <td>Italy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35567</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35568</th>\n",
       "      <td>Bosnia-Herzegovina</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35569</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35570</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35572</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35573</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35574</th>\n",
       "      <td>Korea Republic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35575</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35576</th>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35577</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35578</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35580</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35581</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35582</th>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35583</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35584</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35585</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35587</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35588</th>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35589</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35590</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35592</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35593</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35595</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>836 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     team  score\n",
       "1277              Belgium      0\n",
       "1278               France      4\n",
       "1279               Brazil      1\n",
       "1280                 Peru      1\n",
       "1281            Argentina      1\n",
       "1282                Chile      3\n",
       "1283              Bolivia      0\n",
       "1284             Paraguay      0\n",
       "1286              Uruguay      1\n",
       "1287            Argentina      6\n",
       "1288                Chile      1\n",
       "1289              Belgium      0\n",
       "1290              Bolivia      0\n",
       "1291              Uruguay      4\n",
       "1292            Argentina      3\n",
       "1294            Argentina      6\n",
       "1295              Uruguay      6\n",
       "1296              Uruguay      4\n",
       "1649            Argentina      2\n",
       "1650              Austria      3\n",
       "1651              Belgium      2\n",
       "1652               Brazil      1\n",
       "1653       Czechoslovakia      2\n",
       "1654                Egypt      2\n",
       "1655                Italy      7\n",
       "1656          Netherlands      2\n",
       "1657              Austria      2\n",
       "1658       Czechoslovakia      3\n",
       "1659              Germany      2\n",
       "1660                Italy      1\n",
       "...                   ...    ...\n",
       "35562             Croatia      1\n",
       "35563         Netherlands      2\n",
       "35564          Costa Rica      0\n",
       "35565              Greece      2\n",
       "35566               Italy      0\n",
       "35567               Japan      1\n",
       "35568  Bosnia-Herzegovina      3\n",
       "35569             Ecuador      0\n",
       "35570            Honduras      0\n",
       "35572             Nigeria      2\n",
       "35573             Algeria      1\n",
       "35574      Korea Republic      0\n",
       "35575            Portugal      2\n",
       "35576                 USA      0\n",
       "35577              Brazil      1\n",
       "35578            Colombia      2\n",
       "35580          Costa Rica      1\n",
       "35581         Netherlands      2\n",
       "35582              France      2\n",
       "35583             Germany      2\n",
       "35584           Argentina      1\n",
       "35585             Belgium      2\n",
       "35587              Brazil      2\n",
       "35588              France      0\n",
       "35589           Argentina      1\n",
       "35590         Netherlands      0\n",
       "35592              Brazil      1\n",
       "35593         Netherlands      0\n",
       "35595              Brazil      0\n",
       "35597             Germany      1\n",
       "\n",
       "[836 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_home = df_FIFA[['home_team', 'home_score']]\n",
    "column_update = ['team', 'score']\n",
    "df_score_home.columns = column_update\n",
    "df_score_home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1277</th>\n",
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       "      <td>USA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>Peru</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>Mexico</td>\n",
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       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>Romania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Chile</td>\n",
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       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>USA</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>Germany</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>Spain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>USA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>35562</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35563</th>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35564</th>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35565</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35566</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35567</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35568</th>\n",
       "      <td>Iran</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35569</th>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35570</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35572</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35573</th>\n",
       "      <td>Russia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35574</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35575</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35576</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35577</th>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35578</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35580</th>\n",
       "      <td>Greece</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35581</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35582</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35583</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35584</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35585</th>\n",
       "      <td>USA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35587</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35588</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35589</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35590</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35592</th>\n",
       "      <td>Germany</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35593</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35595</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>836 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              team  score\n",
       "1277           USA      3\n",
       "1278        Mexico      1\n",
       "1279    Yugoslavia      2\n",
       "1280       Romania      3\n",
       "1281        France      0\n",
       "1282        Mexico      0\n",
       "1283    Yugoslavia      4\n",
       "1284           USA      3\n",
       "1286          Peru      0\n",
       "1287        Mexico      3\n",
       "1288        France      0\n",
       "1289      Paraguay      1\n",
       "1290        Brazil      4\n",
       "1291       Romania      0\n",
       "1292         Chile      1\n",
       "1294           USA      1\n",
       "1295    Yugoslavia      1\n",
       "1296     Argentina      2\n",
       "1649        Sweden      3\n",
       "1650        France      2\n",
       "1651       Germany      5\n",
       "1652         Spain      3\n",
       "1653       Romania      1\n",
       "1654       Hungary      4\n",
       "1655           USA      1\n",
       "1656   Switzerland      3\n",
       "1657       Hungary      1\n",
       "1658   Switzerland      2\n",
       "1659        Sweden      1\n",
       "1660         Spain      1\n",
       "...            ...    ...\n",
       "35562       Mexico      3\n",
       "35563        Chile      0\n",
       "35564      England      0\n",
       "35565  Ivory Coast      1\n",
       "35566      Uruguay      1\n",
       "35567     Colombia      4\n",
       "35568         Iran      1\n",
       "35569       France      0\n",
       "35570  Switzerland      3\n",
       "35572    Argentina      3\n",
       "35573       Russia      1\n",
       "35574      Belgium      1\n",
       "35575        Ghana      1\n",
       "35576      Germany      1\n",
       "35577        Chile      1\n",
       "35578      Uruguay      0\n",
       "35580       Greece      1\n",
       "35581       Mexico      1\n",
       "35582      Nigeria      0\n",
       "35583      Algeria      1\n",
       "35584  Switzerland      0\n",
       "35585          USA      1\n",
       "35587     Colombia      1\n",
       "35588      Germany      1\n",
       "35589      Belgium      0\n",
       "35590   Costa Rica      0\n",
       "35592      Germany      7\n",
       "35593    Argentina      0\n",
       "35595  Netherlands      3\n",
       "35597    Argentina      0\n",
       "\n",
       "[836 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_away = df_FIFA[['away_team', 'away_score']]\n",
    "df_score_away.columns = column_update\n",
    "df_score_away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peru</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chile</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Austria</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Italy</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>Iran</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>Russia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>Greece</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>USA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>Germany</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1672 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                team  score\n",
       "0            Belgium      0\n",
       "1             France      4\n",
       "2             Brazil      1\n",
       "3               Peru      1\n",
       "4          Argentina      1\n",
       "5              Chile      3\n",
       "6            Bolivia      0\n",
       "7           Paraguay      0\n",
       "8            Uruguay      1\n",
       "9          Argentina      6\n",
       "10             Chile      1\n",
       "11           Belgium      0\n",
       "12           Bolivia      0\n",
       "13           Uruguay      4\n",
       "14         Argentina      3\n",
       "15         Argentina      6\n",
       "16           Uruguay      6\n",
       "17           Uruguay      4\n",
       "18         Argentina      2\n",
       "19           Austria      3\n",
       "20           Belgium      2\n",
       "21            Brazil      1\n",
       "22    Czechoslovakia      2\n",
       "23             Egypt      2\n",
       "24             Italy      7\n",
       "25       Netherlands      2\n",
       "26           Austria      2\n",
       "27    Czechoslovakia      3\n",
       "28           Germany      2\n",
       "29             Italy      1\n",
       "...              ...    ...\n",
       "1642          Mexico      3\n",
       "1643           Chile      0\n",
       "1644         England      0\n",
       "1645     Ivory Coast      1\n",
       "1646         Uruguay      1\n",
       "1647        Colombia      4\n",
       "1648            Iran      1\n",
       "1649          France      0\n",
       "1650     Switzerland      3\n",
       "1651       Argentina      3\n",
       "1652          Russia      1\n",
       "1653         Belgium      1\n",
       "1654           Ghana      1\n",
       "1655         Germany      1\n",
       "1656           Chile      1\n",
       "1657         Uruguay      0\n",
       "1658          Greece      1\n",
       "1659          Mexico      1\n",
       "1660         Nigeria      0\n",
       "1661         Algeria      1\n",
       "1662     Switzerland      0\n",
       "1663             USA      1\n",
       "1664        Colombia      1\n",
       "1665         Germany      1\n",
       "1666         Belgium      0\n",
       "1667      Costa Rica      0\n",
       "1668         Germany      7\n",
       "1669       Argentina      0\n",
       "1670     Netherlands      3\n",
       "1671       Argentina      0\n",
       "\n",
       "[1672 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score = pd.concat([df_score_home,df_score_away], ignore_index=True)\n",
    "df_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "Germany                 224\n",
       "Brazil                  221\n",
       "Argentina               131\n",
       "Italy                   128\n",
       "France                  106\n",
       "Spain                    92\n",
       "Hungary                  87\n",
       "Netherlands              86\n",
       "Uruguay                  80\n",
       "England                  79\n",
       "Sweden                   74\n",
       "Russia                   66\n",
       "Mexico                   57\n",
       "Yugoslavia               55\n",
       "Belgium                  52\n",
       "Switzerland              45\n",
       "Czechoslovakia           44\n",
       "Poland                   44\n",
       "Portugal                 43\n",
       "Austria                  43\n",
       "Chile                    40\n",
       "USA                      37\n",
       "Korea Republic           31\n",
       "Romania                  30\n",
       "Paraguay                 30\n",
       "Denmark                  27\n",
       "Colombia                 26\n",
       "Scotland                 25\n",
       "Bulgaria                 22\n",
       "Croatia                  21\n",
       "                       ... \n",
       "Iran                      7\n",
       "Norway                    7\n",
       "Korea DPR                 6\n",
       "Ukraine                   5\n",
       "Cuba                      5\n",
       "Slovenia                  5\n",
       "German DR                 5\n",
       "Greece                    5\n",
       "Slovakia                  5\n",
       "Wales                     4\n",
       "New Zealand               4\n",
       "Bosnia-Herzegovina        4\n",
       "Egypt                     3\n",
       "Jamaica                   3\n",
       "Honduras                  3\n",
       "Czech Republic            3\n",
       "Haiti                     2\n",
       "Kuwait                    2\n",
       "United Arab Emirates      2\n",
       "Iraq                      1\n",
       "El Salvador               1\n",
       "Israel                    1\n",
       "Bolivia                   1\n",
       "Togo                      1\n",
       "Angola                    1\n",
       "Indonesia                 0\n",
       "China                     0\n",
       "Congo DR                  0\n",
       "Trinidad and Tobago       0\n",
       "Canada                    0\n",
       "Name: score, Length: 79, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_score = df_score.groupby('team')['score'].sum()\n",
    "s_score.sort_values(ascending=False, inplace=True)\n",
    "s_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc0dea58>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1uW0HThqy91r2w5mkZYC7gPubDEbNzMzMKscBaYki4kXgA2XXw8zMzKxMlZ9DamZmZmbDmwNSMzMzMyuVA1IzMzMzK5UDUjMzMzMrlQNSMzMzMyuVn7JvQZdf+GLZVbBB4XatJrdrdbltq6v12/Yzuy9TdhX6xAFpkyTNB2ZQvNx1PnBwRNw2AOWeAfw4Ih6S9DiwYUT4bbxmZmY2Yjggbd6ciJgEIOnjwHHAlvUZJI2KiPl9KTQivjhwVTQzMzNrPQ5IF8/bgRcAJG0FHA08DUwC1pB0CbASMBo4JSLaJe0AfDcdPwZYKiJWkXQDcHhE3DO0l2BmZmY2PDggbd4YSdMpgswVgW3q9m0MrBURM9P2/hHxvKQxwN2SLoqIy4DLACQFcGOzJ5Y0BZgCEBH9vxIzMzOrtFqtVnYV+sQBafPqh+w3Ac6VtFbad1ddMApwqKSd0/eVgNWA59KxR6SyTmv2xBHRDrSnzbwf12BmZmYjwKxZw+NxlPHjxzeVz699WgwRcTtQA5ZLSa907ktD+NsCm0TEusCfKXpVkfRRYDfgwKGsr5mZmdlw5h7SxSBpIjCK1OvZYBzwQkS8mvJ9OB2zMvBT4BMRMWfIKmtmZmY2zDkgbV7nHFIoXv20b0TMl9SY7yrgQEkPAI8Ad6T0ycCywMXpmI6I+NTiVKTV3i1mzanVasNmiMUGjtu1uty21eW2HXpZnntKYovJOzo6yq6DDQL/B7Ca3K7V5batLrftwElzSLPe8nkOqZmZmZmVygGpmZmZmZXKAamZmZmZlcoBqZmZmZmVygGpmZmZmZXKAamZmZmZlcrvIW1BO/764bKrYGY2oC7da2LZVTCzElW2h1TSzpLytFrSYJ9rgqQ967Y3lDR1sM9rZmZmVgWVDUiBzwG3AHs07pA0aoDPNQF4MyCNiHsi4tABPoeZmZlZJVVyyF7SWGAzYGvgMuAYSVsBRwNPA5OANSR9G9gLeBKYBdwbESdIeh9wGrAc8CpwQEQ8LOls4GVgQ+BdwBERMQ04Hlg9LS16DvBn4PCI2F7SMcB7gVXTz5MjYmqq5yXASsBo4JSIaB/UG2NmZmY2DFUyIAV2Aq6KiEclPS9p/ZS+MbBWRMyUtCGwK7AexX24D7g35WsHDoyI/5X0IeCnwDZp34rA5sBEimB3GvANUgAKkILfehMpguO3AY9I+llEvA7sHxHPSxoD3C3pooh4rvFiJE0BpgBERL9ujJnZcFSr1cquQp+1tbW1ZL2td27boVfVgPRzwMnp+wVp+/fAXRExM6VvDlwaEXMAJF2efo4FNgV+K6mzvKXryr4kIt4AHpK0QpP1+X1EzAXmSnoWWAH4O3CopJ1TnpWA1YBFAtLUc9rZe5o3eU4zs5bRiuuGe73z6nLbDpy0ln2vKheQSlqWojdzLUk5MIoiiPsD8Epd1qybIpYAXoyISd3sn9tEGT0dMx9oS72o2wKbRMSrkm6gGLo3MzMzG1Gq+FDTZ4FzI2LliJgQESsBMyl6ROvdAnxG0ujUK/ppgIh4GZgpaTcASZmkdXs5578ohuP7YhzwQgpGJwIf7uPxZmZmZpVQuR5SiuH54xvSLgIOAv5fZ0JE3C3pMuB+4AngHuCltHsv4GeSvgUsSTHsf38P53wAmCfpfuBsioeaenMVcKCkB4BHgDuaOAbw+/qqykNE1eR2NTPrXZbnI3dKoqSxETFb0luAm4ApEXFf2fXqRd7R0VF2HWwQOHCpJrdrdbltq8ttO3DSHNJepzhWsYe0L9olrUExd/OcFghGzczMzCpnRAekEbFn77nMzMzMbDBV8aEmMzMzM2shDkjNzMzMrFQOSM3MzMysVA5IzczMzKxUDkjNzMzMrFQj+in7gSBpdkSMlTQB2DQizu8l/wTgiohYa3HPOf+AHRb3UBvGnim7AjYo3K4LjPrFZWVXwcyGKfeQDpwJgF8jZWZmZtZH7iEdOMcDq0uaDpwDXAycB7w17T84Im6rP0DSzcAhETE9bd8KHBQRDwxdtc3MzMzK5R7SgfMN4OaImBQRJwHPAh+LiPWB3YGpXRxzBjAZQNIHgKUdjJqZmdlI4x7SwbMk8BNJk4D5wAe6yPNb4NuSvg7sD5zdVUGSpgBTACJiUCprZjbYarVa2VUYUG1tbZW7Jiu4bYeeA9LB858UzzOsS9ET/e/GDBHxqqQ/AjsCAjbsqqCIaAfa02Y+KLU1Mxtks2bNKrsKA6pWq1Xumqzgth0448ePbyqfh+wHzr+At9VtjwOejog3gM8Do7o57gyK4fy7I+L5wa2imZmZ2fDjgHTgPADMk3S/pP8EfgrsK+kOiuH6V7o6KCLuBV4GzhqympqZmZkNI1meewS4TJLGAzcAE1Nvam/yjo6Owa2UlcJDRNXkdq0ut211uW0HThqyz3rL5x7SEknaB7gTOLLJYNTMzMyscvxQU4ki4lzg3LLrYWZmZlYm95CamZmZWakckJqZmZlZqRyQmpmZmVmpHJCamZmZWakckJqZmZlZqfyUfQuaf8AOZVfBBsEzZVfABkV/2nXULy4bsHqYmQ1nDkgbSJoPzKhL2ikiHi+pOmZmZmaV54B0UXMiYlJ3OyW1RcS8oayQmZmZWZU5IG2CpMnAp4HRwFsl7QBcCrwDWBL4VkRcKmkCcCVwC7Ap8BSwY0TMkfR+4OfAcsB8YLeI+H+Svg4IWBq4OCKOHtKLMzMzMyuZA9JFjZE0PX2fGRE7p++bAOtExPOS2oCdI+JlSTXgDkmdk71WAz4XEQdICmBX4FfAr4HjI+JiSaOBJSRtl/JvTLHO62WStoiIm+orJGkKMAUgIgbtws1seKnVamVXwXrQ1tbmNqoot+3Qc0C6qO6G7P8YEc+n7xnwA0lbAG8A7wZWSPtmRkRnQHsvMEHS24B3R8TFABHxb4AUkG4H/DnlH0sRoC4UkEZEO9CeNvN+Xp+ZtYhZs2aVXQXrQa1WcxtVlNt24IwfP76pfA5Im/dK3fe9KIbeN4iI1yU9TjGcDzC3Lt98YAxFANuVDDguIk4f4LqamZmZtQy/h3TxjAOeTcHo1sDKPWWOiJeBv0vaCUDS0pLeAlwN7C9pbEp/t6TlB7nuZmZmZsOKe0gXz6+ByyXdA0wHHm7imM8Dp0v6LvA6xUNN10haHbhdEsBsYG/g2Z4K8rsJq8lDRNXkdjUz612W556S2GLyjo6Osutgg8CBSzW5XavLbVtdbtuBk+aQdjd18U0esjczMzOzUjkgNTMzM7NSOSA1MzMzs1I5IDUzMzOzUjkgNTMzM7NSOSA1MzMzs1L5PaQtaPz0GWVXwQZJcwus2WDrmLR22VUwMxtRHJD2gaQjgT0plgR9A/hSRNzZxzJ2ANaIiOMHoYpmZmZmLccBaZMkbQJsD6wfEXMl1YCl+lpORFwGeKklMzMzs8QBafNWBGZFxFyAiJgFIOlx4EJg65Rvz4h4TNJngG9RBK3PAXtFxDOSJgMbRsTBks4GXgY2BN4FHBER04bukszMzMzK54C0edcAR0l6FPgTcGFE3Jj2vRwRG0vaBziZoif1FuDDEZFL+iJwBPC1LspdEdgcmEjRc7pIQCppCjAFICIG9qrMbBG1Wm3AympraxvQ8mz4cNtWl9t26DkgbVJEzJa0AfARit7QCyV9I+3+Td3Pk9L396Q8K1L0ks7spuhLIuIN4CFJK3Rz7nagPW3m/bsSM+vNQK5h7TWxq8ttW11u24GT1rLvlV/71AcRMT8iboiIo4GDgV3TrvogsfP7qcBPImJt4EvA6G6KnVv3PRvI+pqZmZm1AgekTZL0QUmr1SVNAp5I33ev+3l7+j4OeCp933fwa2hmZmbWmjxk37yxwKmSlgHmAY9RzOvcHlha0p0UAf7nUv5jgN9Kegq4A1hloCridyRWk4eIzMxspMry3FMS+yM9Zb9h51P3QyDv6OgYolPZUHJAWk1u1+py21aX23bgpDmkvU5J9JC9mZmZmZXKQ/b9FBETyq6DmZmZWStzD6mZmZmZlcoBqZmZmZmVygGpmZmZmZXKAamZmZmZlcoBqZmZmZmVakQ9ZS9pdkSMrdueTPEO0YPLq1XfXfjg58uugllL2X3N88qugpmZ9cA9pCWRNKL+MWBmZmbWHQdFiaSzgSsiYlranh0RYyVtRbEM6CxgLeBeYO+IyCV9Cvhx2ncfsGpEbC9pY+BkYAwwB9gvIh5JPbKfBkYDb03Lik6LiEvTOX8NXBgRlw3RZZuZmZmVbqT1kI6RNL3zA3y3yePWAw4D1gBWBTaTNBo4HfhkRGwOLFeX/2Fgi4hYDzgK+EHdvk2AfSNiG+AMYD8ASeOATYE/LPbVmZmZmbWgkdZDOiciJnVudM4hbeK4uyLi7+mY6cAEYDbwfxExM+X5DTAlfR8HnCNpNSAHlqwr648R8TxARNwo6TRJywO7ABdFxLzGk0ua0ll2RDR5qWbWqVarlXbutra2Us9vg8dtW11u26E30gLSnswj9RhLyoCl6vbNrfs+n+K+ZT2UdSxwfUTsLGkCcEPdvlca8p4H7AXsAezfVWER0Q60p828p4sws0XNmjWrtHPXarVSz2+Dx21bXW7bgTN+/Pim8o20IfuePA5skL7vyMK9ml15GFg1BZwAu9ftGwc8lb5P7qWcsymmAxARDzZVUzMzM7MKcUC6wC+ALSXdBXyIRXsyFxIRc4AvA1dJugV4Bngp7f4RcJykW4FRvZTzDPBX4Kz+Vd/MzMysNWV57hHgxSVpbETMTkP8pwH/GxEn9bGMtwAzgPUj4qXe8gN5R0fHYtTWhjsPEVWT27W63LbV5bYdOGnIvqdpjoB7SPvrgPSQ04MUw/Sn9+VgSdtSDP2f2mQwamZmZlY57iFtPe4hrSj/i7ya3K7V5batLrftwHEPqZmZmZm1BAekZmZmZlYqB6RmZmZmVioHpGZmZmZWKgekZmZmZlYqLx3agpZ/7L/LroINhsdg+bLrUDHPvv+4sqtgZmZNaIkeUkm5pBPrtg+XdEwvx2wladO67bMlfbaf9XhcUq0/ZdSVNXsgyjEzMzNrdS0RkAJzgV36GAxuBWzaW6ZmSMoktcq9MjMzM2sprTJkPw9oB/4TOLJ+h6TlgJ8D701JhwFPAQcC8yXtDRyS9m0h6avAu4AjImJaKuPrgIClgYsj4mhJE4ArgeuBTYCdGs57CbASMBo4JSLaU/ps4BRge2AOsGNEPCNpFeB8int+VV05KwIXAm9P+w6KiJsX+06ZmZmZtZhWCUihWCv+AUk/akg/BTgpIm6R9F7g6ohYXdLPgdkRcQKApC8AKwKbAxOBy4BpkrYDVgM2plhJ4DJJWwB/Az4I7BcRX05l1J93/4h4XtIY4G5JF0XEc8BbgTsi4shU1wOA76V6/iwizpX0H3Xl7Jnq/H1Jo4C3NF64pCnAFICIWJx7ZzYi1WoDMsOmX9ra2oZFPWzguW2ry2079FomII2IlyWdCxxK0fPYaVtgjbpg8e2S3tZNMZdExBvAQ5JWSGnbpc+f0/ZYigD1b8ATEXFHN2UdKmnn9H2ldMxzwGvAFSn9XuBj6ftmwK7p+3nAD9P3u4EzJS2Z6je9i2tvp+ghBvBar2ZNGg5L/3kJwupy21aX23bgpKVDe9UyAWlyMnAfcFZd2hLAJhFRH6Q29mZ2mlv3Pav7eVxEnN5w/ATgla4KkbQVRSC8SUS8KukGiqF7gNcjojNonM/C93iRYDIibko9sp8GzpP0PxFxblfnNTMzM6uilnpQJyKeBwL4Ql3yNcDBnRuSJqWv/wK66ymtdzWwv6Sx6fh3S+rt7TvjgBdSMDoR+HAT57kV2CN936uuvisDz0bEL4BfAus3UZaZmZlZZbRaDynAidQFoBRD+KdJeoDiem6ieKDpcoo5ojuy4KGmRUTENZJWB25Pvaqzgb0peje7cxVwYDrnI0B3w/r1vgKcL+krwEV16VsBX5f0ejr3Pr0V5HcrVpOHiMzMbKTK8txTEltM3tHRUXYdbBA4IK0mt2t1uW2ry207cNIc0qy3fC01ZG9mZmZm1eOA1MzMzMxK5YDUzMzMzErlgNTMzMzMSuWA1MzMzMxK5YDUzMzMzErViu8hHfGmTp1adhXMhtShhx5adhXMzGzqWRI8AAAgAElEQVQQjageUkkTJP2lIe0YSYeXVSczMzOzkW5EBaTNkuSeYzMzM7Mh4sArkXQDcBuwGXCZpLWBKyJiWto/OyLGSloC+AmwJTCTIqg/MyKmSXoc2DAiZknaEDghIraStDFwMjAGmAPsFxGPSLoZOCQipqdz3AocFBEPDOGlm5mZmZXKAenClomILQEknd1Nnl2ACcDawPLAX4Ezeyn3YWCLiJgnaVvgB8CuwBnAZOAwSR8Alu4qGJU0BZgCEBF9uyKzCqjVamVXYbG1tbW1dP2te27b6nLbDr2RFpDmvaRf2EQZmwO/jYg3gH9Iur6JY8YB50haLZ1ryZT+W+Dbkr4O7A+c3dXBEdEOtDfU1WzEaOU1pb0mdnW5bavLbTtw0lr2vRppc0ifA97RkPZOoPO37pW69Hmk+yMpA5ZK6VkP5b95DDC6Lv1Y4PqIWAv4TOe+iHgV+COwIyDg/D5ci5mZmVkljKiANCJmA09L+iiApHcCnwBu6SL748AG6fuOLOjVvAXYVdISklYAturmmF3r0scBT6XvkxvOcwYwFbg7Ip7v0wWZmZmZVcBIG7IH2Ac4TdKJafs7EfH/JDXm+wVwqaS7gGtZ0Ht6EfBR4C/Ao8CdwEudZQG/lPTNlN7pRxRD9l8Frqs/SUTcK+ll4KxmL8DvZKwmDxGZmdlIleW5pyT2laSxETFb0rLAXcBmEfGPxSxrPHADMDHNS+1N3tHRsTinsmHOAWk1uV2ry21bXW7bgZPmkPY03REYmT2kA+EKSctQzCs9th/B6D7A94GvNhmMmpmZmVWOe0hbj3tIK8r/Iq8mt2t1uW2ry207cJrtIR1RDzWZmZmZ2fDjgNTMzMzMSuWA1MzMzMxK5YDUzMzMzErVp6fsJU0A1gHG1qdHhFcYGkKXX/hi2VWwQeF2rfeZ3ZcpuwpmZjZEmg5IJf03cBTwIDCnbldOiy15KWk+MKMu6YKIOH4xy5odEWN7z9lrOROAK9LyomZmZmYjRl96SL8GbBARDw1WZYbQnIiYVHYlzMzMzKxvAelzFGu1V5akx4FzgM9QrF2/W0Q8LGk5il7gZYG7gU9QBOez6o4dC1wKvCMd+62IuDT1fF4J3AJsSrGm/Y4RMUfSBsCZwKtpv5mZmdmI05eHmg4D2iVtKOm99Z/BqtwgGiNpet1n97p9syJifeBnwOEp7WjgupR+MdDVNf8b2Dnl2Ro4UVLni2BXA06LiDUpJgrumtLPAg6NiE0G9OrMzMzMWkhfekiXArYD9mxIz4FRA1ajodHTkP3v0s97gV3S982BnQEi4ipJL3RxXAb8QNIWwBvAu4EV0r6ZETG9rtwJksYBy0TEjSn9POCTXVVI0hRgSjp/E5dn1vpqtVrZVRgQbW1tlbkWW5jbtrrctkOvLwHpT4FvAhew8ENNVTM3/ZzPgvvT65JXwF7AchRD+a+n4f/RDWV2ljsmldnUuq0R0Q60p02v9WojQlWW7fMShNXltq0ut+3ASUuH9qovAWkbcFZEzF+sGrW2WwABP5S0HcU80UbjgGdTMLo1sHJPBUbEi5JekrR5RNxCEdCamZmZjTh9CUhPAL4h6QcR0eq9dGMkTa/bvioivtFD/u8Av0lzTW8Engb+1ZDn18Dlku4BpgMPN1GP/YAzJb0KXN107c3MzMwqJMvz5mJLSU8C7wJeo3ji/k0R0YoPNjVN0tLA/IiYJ2kT4GclvjYq7+joKOnUNpg8RFRNbtfqcttWl9t24KQh+16nPvalh3Tvxa5N63svEJKWoAjIDyi5PmZmZmaV0XQPqQ0b7iGtKP+LvJrcrtXltq0ut+3AGYweUiRNAj4C1OoLj4ij+lg/MzMzMzOgDy/GT+/CvBXYBvgvYG2K5UTfPzhVMzMzM7ORoC8rNR0BfCIidqZ4sfzOwGeB1welZmZmZmY2IvQlIF0+Im5O39+QtEREXEmx7ruZmZmZ2WLpS0D6d0kT0vdHgR0lfYTiqXMzMzMzs8XSl4eafgSsDjwOfBeYRrG+/aEDX62hIelIYE+K5TzfAL4UEXcOUNmzI2LsQJTVaMdfN/POfbPh59K9JpZdBTMzG4aaDkgj4uy671dKegewVETMHoyKDbb0gvvtgfUjYq6kGkWAbWZmZmZDqK+vfVoW+BSwYkT8SFJN0jIR8ffBqd6gWhGYFRFzASJilqSNJf0kInaRtCNwAcUa9UsAD0XEqpLeB5wGLAe8ChwQEQ9LWgU4n+KeXlV/IklfBwQsDVwcEUen6Q9XArcAmwJPATtGxJxBv3IzMzOzYaQvr33aEngE2Av4dkpeDfjZINRrKFwDrCTpUUk/Tdd3H7Be2v8R4C/ARsCHgM6h/HbgkIjYADgc+GlKP4ViSdGNgH90nkTSdhT3aWNgErCBpC3S7tWA0yJiTeBFYNdBuVIzMzOzYawvPaQnA7tHxLWSXkhpd1IEWi0nImZL2oAi8NwauBD4BvCYpNUpruvHwBbAKOBmSWMpejN/K6mzqKXTz81YEFCeB/wwfd8uff6ctsdSBKJ/A2ZGxPSUfi8woau6pnfATkn1XuxrNitbrVYruwpDrq2tbURe90jgtq0ut+3Q60tAOiEirk3fO9cbfa2PZQwrETEfuAG4QdIMYF/gZuCTFO9X/RNwNkVAejhFj/KLETGpmyK7Woc1A46LiNPrE9OQ/dy6pPnAmG7q2U7RM9vdOcxawkhcis9LEFaX27a63LYDJy0d2qu+vPbpIUkfb0jbFpjRhzKGDUkflLRaXdIk4AngJuAw4PaI+CewLDAReDAiXgZmStotlZFJWjcdfyuwR/q+V125VwP7p95VJL1b0vKDdV1mZmZmraYvvZtfBX4v6ffAGEmnU7wUf8dBqdngGwucKmkZYB7wGMWw+CvAChSBKcADwLMR0dkzuRfwM0nfApakePDpfuArwPmSvgJc1HmSiLgmTQG4PQ3zzwb2pugRNTMzMxvxsjxvbgRY0uEUT5HvDawMPAn8ClBE/HjQamiN8o6OjrLrYIPAQ0TV5HatLrdtdbltB04ass96y9eXHtKjIuIEihfkvyn1FDogNTMzM7PF0mtAKmmb9HWUpK1ZOMpdFfjXYFTMzMzMzEaGZnpIf5l+jgbOrEvPKd63echAV8rMzMzMRo5eA9KIWAVA0rkRsc/gV8nMzMzMRpKmX/vkYNTMzMzMBkNf3kNqZmZmZjbgHJCamZmZWaladtnPkWz+ATuUXQUbBM+UXYFujPrFZWVXwczMKs4BaRckzadYErUNmAl8PiJeHKCyNwT2iYhDB6I8MzMzs1bngLRrcyJiEoCkc4D/AL4/EAVHxD3APQNRlpmZmVkVOCDt3e3AOgCStgIOj4jt0/ZPgHsi4mxJxwM7APOAayLicEm7AUdTrFv/UkRsUV+GpI2Bk4ExwBxgv4h4ZGgvz8zMzKxcDkh7IGkU8FEWLA7QXb53AjsDEyMil7RM2nUU8PGIeKourd7DwBYRMU/StsAPgF27KH8KMAUgIhb7eswWR61WK7sKLa2trc33sKLcttXlth16Dki7NkbSdGACcC/wx17yvwz8GzhD0u+BK1L6rcDZkgL4XRfHjQPOkbQaxcpXS3ZVeES0A+1pM+/DdZj126xZs8quQkur1Wq+hxXltq0ut+3AGT9+fFP5/NqnrnXOIV0ZWIpiDikUw/H192w0QETMAzYGLgJ2Aq5K6QcC3wJWAqZLWrbhPMcC10fEWsBnOsszMzMzG0kckPYgIl4CDgUOl7Qk8ASwhqSlJY2jGM5H0lhgXET8ATgM6Hwg6n0RcWdEHAXMoghM640DnkrfJw/29ZiZmZkNRx6y70VE/FnS/cAeEXFeGn5/APhf4M8p29uASyWNBjLgP1P6/6Th+Ay4Frgf2LKu+B9RDNl/Fbiu2Tr5vZDV5CEiMzMbqbI895TEFpN3dHSUXQcbBA5Iq8ntWl1u2+py2w6cNIc06y2fh+zNzMzMrFQOSM3MzMysVA5IzczMzKxUDkjNzMzMrFQOSM3MzMysVA5IzczMzKxUfg9pC5p/wA5lV8EGwTP9PN7vpzUzs1blgLSOpBz4VUR8Pm23AU8Dd0bE9otR3g7AGhFx/MDW1MzMzKw6HJAu7BVgLUljImIO8DEWLO3ZZxFxGeBuKzMzM7MeOCBd1JXAp4FpwOeA3wAfAZD0VuBUYG2Ke3dMRFyalv5cKyL2l7R2OmZjQMCGEXGwpBWAnwOrpvMcFBG3pWP3T2lnRMTJQ3KVZmZmZsOEA9JFXQAcJekKYB3gTFJAChwJXJcCz2WAuyT9CTgZuEHSzinPlyLiVUn15U4FboyInSWNAsZK2gDYD/gQxbJad0q6MSL+XH+gpCnAFICIGJyrtpZXq9XKroJ1oa2tzW1TUW7b6nLbDj0HpA0i4gFJEyh6R//QsHs7YAdJh6ft0cB7I+KvkiYDDwCnR8StXRS9DbBPOsd84CVJmwMXR8QrAJJ+RxH8LhSQRkQ70J428/5doVWV110enrwmdnW5bavLbTtw0lr2vXJA2rXLgBOArYBl69IzYNeIeKSLY1YDZgPN3fkF5ZmZmZmNaH4PadfOBL4bETMa0q8GDpGUAUhaL/0cB5wCbAEsK+mzXZR5LXBQyj9K0tuBm4CdJL0lzU/dGbh5MC7IzMzMbLhyQNqFiPh7RJzSxa5jgSWBByT9JW0DnAT8NCIeBb4AHC9p+YZjvwJsLWkGcC+wZkTcB5wN3AXcSfFQ058xMzMzG0GyPPeUxBaTd3R0lF0HGwSes1RNbtfqcttWl9t24KQ5pL1OUXQPqZmZmZmVygGpmZmZmZXKAamZmZmZlcoBqZmZmZmVygGpmZmZmZXKAamZmZmZlcoBqZmZmZmVqqWXDk0rJt0MfD8irkxpAvaPiE8M4nnbgFkRscxiHLsScEJE7L645x8/vXEBKauKvqw72zFp7UGrh5mZ2VBq6YA0InJJBwK/lXQ9MAr4PjBowWh/RcSTwGIHo2ZmZmZV09IBKUBE/EXS5cB/AW8FzgUySdMjYhKApG8AbRHxPUkfBtqB2cCtwMciYpKkMcDPgfWB14HDIuImSWtTrG2/JMUUh52Av3WeP61JfwmwDMX9/GZEXCHpROCRiGhP+b4H/BP4PTAtnfN9FEuHjgXeAL4cEXcO2s0yMzMzG4aqMof0O8CewCeBH/WS9yzgixGxKQsvZXUo8FpErA18HjhP0lLAlymG2CcBGwGN63bOAXaMiPWBbSnWtQe4gIV7QncDfttw7NMUAfF6wF7A1N4u1MzMzKxqWr6HFCAiXpF0ITA7IuYW00gXJakGLBURd6Wk8ymCSIDNgf9J5T0oqQN4P3Ab8C1JKwO/i4jH0hzSThnwQ0mbU/RyriSpFhF3S1pJ0grAe4B/RESHpPfXHbs08BNJ6wLzgPd1U+8pwJRUt77cGquwWq1WdhWsCW1tbW6rinLbVpfbduhVIiBN3kgfKIK7+t7f0SktazyoTpf7IuI8SbcDnwb+KGlfiiC10z7AOGD9iJgn6e/pfAAXAbsCEyh6TBt9DXgS2JtiSsDsburQTjHNACDv4RpsBJk1a1bZVbAm1Go1t1VFuW2ry207cMaPb+5x3aoM2Tf6BzBe0jskjaYIJomIfwKvS9ow5duj7pibKIbNkbQ6sCLwmKRVI+KxiDiFYv7nOg3nGgc8m4LRjwHvrtt3QTrHLhTBaaNxwNMRkQP70nPAbGZmZlZJlQxII+LfwA+Au4HLgIfqdu8PnCXpNooe1ZdS+qnAGEkzgF8D+0TEa8Cekh6UNB1YFfhVw+nOAzaVdA/FPNH/ravH/cBywMyIeLaLqv4E+KKkO4CVgbn9uGwzMzOzlpTl+cgaAZY0NiJmp+9HAu+MiK+VXK2+yDs6Gp+rsirwEFE1uV2ry21bXW7bgZOG7HsdAa7SHNJm7SDpCIprfxyYXGptzMzMzEa4EReQRsT5FE/Xm5mZmdkwUMk5pGZmZmbWOhyQmpmZmVmpHJCamZmZWakckJqZmZlZqRyQmpmZmVmpRtxT9lVw4YOfL7sKNgR2X/O8sqtgZmY2JEZkQCppPjCD4kWt84GDI+K2Xo6ZHRFje8lzBvDjiHiop3xmZmZmtsCIDEiBORExCUDSx4HjgC37W2hEfLG/ZZiZmZmNNCM1IK33duCFzg1JXwcELA1cHBFH12eWtATFGvRbAjMp5uGeGRHTJN0AHB4R99T3qEr6LLB9REyWdDYwB5hIsX79fsC+wCbAnRExeRCv1czMzGzYGakB6RhJ04HRwIrANgCStgNWAzamGM6/TNIWEXFT3bG7ABOAtYHlgb8CZ/bx/O9I59wBuBzYDPgicLekSRExvT6zpCnAFICI6OOprFXVarWyq2ADoK2tzW1ZUW7b6nLbDr2RGpDWD9lvApwraS1gu/T5c8o3liJArQ9INwd+GxFvAP+QdP1inP/yiMglzQCeiYgZqS4PUgS7CwWkEdEOtKfNfDHOZy1o1qxZZVfBBkCtVnNbVpTbtrrctgNn/PjxTeUbqQHpmyLidkk1YDmKXtHjIuL0Hg7Jmiy6PnAc3bBvbvr5Rt33zu0R3yZmZmY2soz495BKmgiMAp4Drgb2l9Q59/PdkpZvOOQWYFdJS0haAdiqm6KfkbR6mnO68+DU3szMzKz1jdTeuM45pFD0eO4bEfOBayStDtwuCWA2sDfwbN2xFwEfBf4CPArcCbzUxTm+AVwBPJny9vjKqL7w+ymryUNEZmY2UmV57imJfSVpbETMlrQscBewWUT8Y4hOn3d0dAzRqWwoOSCtJrdrdbltq8ttO3DSHNJepzuO1B7S/rpC0jLAUsCxQxiMmpmZmVWOA9LFEBFblV0HMzMzs6oY8Q81mZmZmVm5HJCamZmZWakckJqZmZlZqRyQmpmZmVmp/FBTC1r+sf8uuwrWhGfff1zZVTAzM2sJLReQSjoS2BOYT7HU5pci4s5ejvkucFNE/EnSYUB7RLw6AHU5BpgdEScMQFlnA1dExLT+lmVmZmbWSloqIJW0CbA9sH5EzE1r0C/V23ERcVTd5mHAr4B+BaSSWuremZmZmQ1XrRZUrQjMioi5ABExS9LGkn4SEbtI2hG4ABhHMT/2oYhYtbP3ERifPtdLmgWcBHw3lT0GWCoiVpG0AfBjiuU+ZwGTI+JpSTcAtwGbAZfVV0zSAcAUigD5MeDzEfFqOvfLwIbAu4AjImKapAw4FdgGmEkTqxiYmZmZVVGrBaTXAEdJehT4E3AhcCuwXtr/EYp14zeiuLaFhvIjYqqkrwJbR0TnmmCXAUgK4EZJS1IEijtGxD8l7Q58H9g/5V8mIrZMxxxTV/zvIuIXKf17wBdSOVAE0psDE9P5pgE7Ax8E1gZWAB4CzuzqoiVNoQh2iYhm7pMNA7VarU/529ra+nyMDX9u1+py21aX23botVRAmtaP34Ai8NyaIiD9BvCYpNWBjSl6NrcARgE3N1OupCOAORFxmqS1gLWAP0oilfN0XfYLuylmrRSILkPRs3p13b5LIuIN4CFJK6S0LYDfRMR8oEPSdT1cdzvQnjbzZq7JytfXdZC9dnI1uV2ry21bXW7bgZPWsu9VSwWkACmAuwG4QdIMYF+KwPOTwOsUPadnUwSSh/dWnqSPArtRBIhQDJ0/GBGbdHPIK92knw3sFBH3S5oMbFW3b27d9/qheQeXZmZmNuK11HtIJX1Q0mp1SZOAJ4CbKB5Wuj0i/gksSzE8/mAXxfwLeFsqb2Xgp4AiYk7a/wiwXHqACklLSlqzieq9DXg6Dfnv1UT+m4A9JI2StCJFj6+ZmZnZiNNqPaRjgVMlLQPMo3h4aApFr+UKFEEewAPAsxHRVQ9kO3ClpKcpelqXBS5Ow/MdEfEpSZ8FpkoaR3GPTqbr4LbetynmrD4BzCAFvT24mOKBphnAo8CNveR/k99vaWZmZlWS5blHjVtM3tHRUXYdbBB4zlI1uV2ry21bXW7bgZPmkPb6JqGWGrI3MzMzs+pxQGpmZmZmpXJAamZmZmalckBqZmZmZqVyQGpmZmZmpXJAamZmZmalckBqZmZmZqUashfjS3oXxQvmN6JYSvNx4LCIeLSf5R4DzI6IE/pZzg3A4RFxT3/KaShzdkSMbUgbD0yNiM8ubrlTp07td91scBx66KFlV8HMzKzlDElAKimjWJnonIjYI6VNolhdqV8BaauJiA5gsYNRMzMzs6oZqh7SrYHXI+LnnQkRMV3SdyWdmpKWA66JiP0k7Q0cCixFsRznlyNivqRPAD8ARgGzIuKj6dg1Ug/ne4GTI2IqgKSvAvunPGdExMmS3goE8J5UzrERcWF9ZSV9DvgmxcoCv4+I/5J0ELBKRByR8kwGNoiIQyRdAqwEjAZOiYj2hvJqwOXA9yiWIL0iItaSNAE4D3hrynpwRNzWx3trZmZm1tKGag7pWsC9jYkRcVRETAK2BJ4DfiJpdWB3YLO0bz6wl6TlgF8Au0bEusBudUVNBD4ObAwcLWlJSRsA+wEfAj4MHCBpPeATFGvWrxsRawFX1dcpDan/kGKd+UnARpJ2AqYBu9Rl3R3oDGT3j4gNgA3h/7d37zFyVvcZx79b1uFmCwemUExRQBUJoSQxEBJTwq0tbUgptpX0ARdRhyA7pFBa0lQCQhuXVilRaUPalFTGgEPiFB4VUztAgIqmXJoYE8CBchGhAVqyFo7F3ebmZfvHe1Ye1p7dtXe8787s85FGO3PmnPOed356Vz+dc+YdzpO0V1N/+wA3A39h++YhH8E64ETbh5f+shYfERERk8647SFtpSznLwO+avt+SecCRwD3SQLYlSpxmwXcZfspANvPN3Vzs+03gDckraPaCvAx4EbbG8pxlgPHUCWgl0n6CtVM5d1DhnQk8J+2f17aLQOOtf1vkn4qaRbwE+B9wH+VNudJmlue7w8cRJVgTwHuAM6xfedWTn8KVRI+mHi/t8VntBBYWM671UcZE0Cj0djutr29vWNqHxNT4tq9EtvuldiOv/FKSB+h9b7JRcCztq8pr3uo9ppe2FxJ0inAQIs+3mh63k91Xj1bq2j7iTJ7+gngbyTdbvuSpipbbVdcDwh4nCrZHZB0PPCbwFG2N5atA7uU+puoZoZ/G9haQno+8BzwIarZ6tdbjHkxMLgNoNVnEBPA+vXrt7tto9EYU/uYmBLX7pXYdq/Etn1mzJgxqnrjtWT/H8DOkhYMFkg6UtKXgBOp9osOugP4lKS9S709Jb0H+CFwnKQDB8tHOOZdwBxJu5V9o3OBu8uS/Ebb3wYuAw4f0u7ecpyGpJ2AeWxOJpcDc0rZ4HL9HsALJRk9mGomd9AA1R7WgyVdsJUx7gGstf02cAbVntaIiIiISWVcElLbA1QJ4YmS/kfSI1Qzo8cDM4DVktZIusT2o8DFwO2SHgL+Hdi3LKEvBJZL+jGbE8JWx3wAWAqspkoyl9h+EPjA4PGAL1J90ai53VrgQuD7wI+BB2yvKO+9ADwKvMf26tLkVqC3jPWvgFVD+usHTgNOkPSHQ4Z5BTBf0iqq5foNw51TRERERDfqGRjICnCHGejr66t7DLEDZImoOyWu3Sux7V6JbfuUJfvhtkMC+aWmiIiIiKhZEtKIiIiIqFUS0oiIiIioVRLSiIiIiKhVEtKIiIiIqFUS0oiIiIioVRLSiIiIiKhV7b9lH9vuu9e/WPcQooXfPXV63UOIiIjoOElIh5DUDzxM9dk8Bsy3vXGY+q/antqG4x4A3GT70LH2FREREdFJsmS/pddszyyJ4ZvA2XUPKCIiIqKbZYZ0eHcDHwSQ9HngM6V8ie3LmytKmgqsAN4NTAEutr2izHx+D7gH+DXgZ8Bs269JOgK4GthY3o+IiIiYdJKQtiCpFzgJuLUkjmcCH6X6PdZ7Jd1p+8GmJq8Dc22/LKkBrJK0srx3EDDP9gJJBj4JfBu4Bvgj23dK+tthxrIQWAhgu70nGm3VaDS2u21vb++Y2sfElLh2r8S2eyW24y8J6ZZ2lbSmPL8buAr4HHCj7Q0AkpYDxwDNCWkP8GVJxwJvA/sB+5T3nrI92Of9wAGS9gCm276zlH+LKgHegu3FwOLycmCM5xc70Pr167e7baPRGFP7mJgS1+6V2HavxLZ9ZsyYMap6SUi39Jrtmc0FknpG0e504BeBI2y/JelpYJfy3htN9fqBXakS2CSXERERMenlS02jcxcwR9JuknYH5lLNnjbbA1hXktETgPcM16HtF4GXJH2sFJ3e7kFHREREdILMkI6C7QckLQVWl6IlQ/aPAiwDvivpR8Aa4PFRdH0mcLWkjcBtox1P7nUZERER3aRnYCCrxh1moK+vr+4xxA6QPUvdKXHtXolt90ps26fsIR1x62OW7CMiIiKiVklIIyIiIqJWSUgjIiIiolZJSCMiIiKiVklIIyIiIqJWSUgjIiIiola5D2kHmr1sNLc4jfG04vSD6x5CREREx0pCOoSkucBy4P22tznzkzQHeML2oy3ePxvYaPvasY00IiIiojskId3SPOAe4DRg0Xa0nwPcBGyRkErqtf3PYxpdRERERJdJQtpE0lTgaOAEYCWwSNLxwBdsn1zqfB34ke2lki4FTgE2AbdTzayeAhwn6WLgk8BVwA9KvyslTQNetX2ZpAXAQuBdwJPAGbY3jtsJR0REREwASUjfaQ5wq+0nJD0v6fBWFSXtCcwFDrY9IGm67RclrQRusv2vpR7AdNvHldeLmrpZbvvKUv7XwFnAP27lWAupEldst+E0o90ajcaY++jt7W1LPzGxJK7dK7HtXont+EtC+k7zgMvL8+vK65tb1H0ZeB1YIulmqmX6Vq5vUX5oSUSnA1OB27ZWyfZiYHF5OTDMcaIm7fjN4/x2cndKXLtXYtu9Etv2Kb9lP6Lc9qmQtBfw61QJ5tPAnwGnAv2883PaBcD2JuAjwA2UmdVhut/QonwpcK7tDwB/OTY7C/YAAAb0SURBVNh3RERExGSSGdLNPgVca/uzgwWS7ixPD5G0M1XC+BvAPWW/6W62b5G0imoPKMArwLRRHnMasFbSFOB04GdtOI+IiIiIjpKEdLN5wKVDym4Afh8w8BDwE+DB8t40YIWkXYAe4PxSfh1wpaTzqJLc4fw5cC/wDPAwo0xkc8/LiIiI6CY9AwPZkthhBvr6+uoeQ+wA2bPUnRLX7pXYdq/Etn3KHtKekeplD2lERERE1CoJaURERETUKglpRERERNQqCWlERERE1CoJaURERETUKglpRERERNQqCWlERERE1GrS3RhfUj/VTeh7gceA+bY3bkP7i2x/eQeM6wDgJtuHjlS3f8Ep7T58jNFOV66sewgREREdazLOkL5me2ZJ/N4Ezh5NI0k9kn4BuGiHji4iIiJikpl0M6RD3A18EEDS54HPlPIlti8vs5bfA74PHAWsAXaVtAZ4BPgiTbOakr4ATLW9SNKRwFXABuAe4CTbh5Y+vwXsXo51ru0f7PAzjYiIiJigJuMMKQCSeoGTgIclHQGcCXwUmAUskHRYqfo+4Frbh9k+k80zrKePcIhrgLNtHwX0N5WvA060fThwKvAP7TuriIiIiM4zGWdIB2c4oZohvQr4HHCj7Q0AkpYDxwArgWdsr9qWA0iaDkxrmvn8DnByeT4F+LqkmVSJ6ntH0d9CYCGA7W0ZSoyTRqMx5j56e3vb0k9MLIlr90psu1diO/4mY0L6mu2ZzQWSeoapv2GY9zbxzlnmXcrf4fo7H3gO+FBp+/owdQGwvRhYXF4OjFQ/xt/69evH3Eej0WhLPzGxJK7dK7HtXolt+8yYMWNU9Sbtkv0QdwFzJO0maXdgLtXs6da8JWlKef4csLekvSTtTJkFtf0C8IqkWaXeaU3t9wDW2n4bOAPYqc3nEhEREdFRkpACth8AlgKrgXupvtT0YIvqi4GHJC2z/RZwSWlzE/B4U72zgMWSfkg1Y/pSKb8CmC9pFdVy/XAzsBERERFdr2dgICvAO4KkqbZfLc8vAPa1/cdt6Hqgr6+vDd3ERJMlou6UuHavxLZ7JbbtU5bsh9vKCEzOPaTj5XckXUj1GT8DfLre4URERERMTElIdxDb1wPX1z2OiIiIiIkue0gjIiIiolbZQ9p5ErCIiIjoJCPuIc0MaYeRdD9VYPPoskdi252PxLV7H4lt9z4S27Y/RpSENCIiIiJqlYQ0IiIiImqVhLTzLB65SnSoxLY7Ja7dK7HtXontOMuXmiIiIiKiVpkhjYiIiIha5cb4HUTSx4GvATsBS2xfWvOQYjtJehp4BegHNtn+sKQ9qX5M4QDgaUC2X6hrjDE6kq4GTgbW2T60lG01lpJ6qK7hTwAbgU/bfqCOccfIWsR2EbAA+HmpdpHtW8p7FwJnUV3X59m+bdwHHSOStD9wLfBLwNvAYttfy3Vbr8yQdghJOwH/BJwEHALMk3RIvaOKMTrB9kzbHy6vLwDusH0QcEd5HRPfUuDjQ8paxfIk4KDyWAh8Y5zGGNtnKVvGFuCr5dqd2ZSMHgKcBvxqaXNF+b8dE88m4E9tvx+YBZxT4pfrtkZJSDvHR4Anbf/U9pvAdcDsmscU7TUb+GZ5/k1gTo1jiVGyfRfw/JDiVrGcDVxre8D2KmC6pH3HZ6SxrVrEtpXZwHW237D9FPAk1f/tmGBsrx2c4bT9CvAYsB+5bmuVhLRz7Af8X9PrZ0tZdKYB4HZJ90taWMr2sb0Wqn+YwN61jS7GqlUscx13h3MlPSTpaknvLmWJbQeSdABwGHAvuW5rlYS0c2ztlw5yi4TOdbTtw6mWgs6RdGzdA4pxkeu4830D+BVgJrAW+LtSnth2GElTgRuAP7H98jBVE9txkIS0czwL7N/0+peBvprGEmNku6/8XQfcSLW099zgMlD5u66+EcYYtYplruMOZ/s52/223wauZPOyfGLbQSRNoUpGl9leXopz3dYoCWnnuA84SNKBkt5FtXl+Zc1jiu0gaXdJ0wafA78F/DdVPOeXavOBFfWMMNqgVSxXAn8gqUfSLOClwSXC6AxD9g7Opbp2oYrtaZJ2lnQg1RdgVo/3+GJk5VvzVwGP2f77prdy3dYot33qELY3SToXuI3qtk9X236k5mHF9tkHuFESVNfgd2zfKuk+wJLOAv4X+L0axxijJOlfgOOBhqRngS8Bl7L1WN5CdeuYJ6luH3PmuA84Rq1FbI+XNJNqyfZp4LMAth+RZOBRqm9xn2O7v45xx4iOBs4AHpa0ppRdRK7bWuWXmiIiIiKiVlmyj4iIiIhaJSGNiIiIiFolIY2IiIiIWiUhjYiIiIhaJSGNiIiIiFolIY2IiIiIWiUhjYiIiIhaJSGNiIiIiFr9PxLwkNb0JSu5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc0ec748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_score.sort_values(ascending=True, inplace=True)\n",
    "s_score.tail(20).plot(kind='barh', figsize=(10,6), title='Top 20 in Total Scores of World Cup')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2018年世界杯32强分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一组：俄罗斯、德国、巴西、葡萄牙、阿根廷、比利时、波兰、法国\n",
    "\n",
    "第二组：西班牙、秘鲁、瑞士、英格兰、哥伦比亚、墨西哥、乌拉圭、克罗地亚\n",
    "\n",
    "第三组：丹麦、冰岛、哥斯达黎加、瑞典、突尼斯、埃及、塞内加尔、伊朗\n",
    "\n",
    "第四组：塞尔维亚、尼日利亚、澳大利亚、日本、摩洛哥、巴拿马、韩国、沙特阿拉伯\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 判断是否有队伍首次打入世界杯 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iceland\n",
      "Panama\n"
     ]
    }
   ],
   "source": [
    "team_list = ['Russia', 'Germany', 'Brazil', 'Portugal', 'Argentina', 'Belgium', 'Poland', 'France', \n",
    "             'Spain', 'Peru', 'Switzerland', 'England', 'Colombia', 'Mexico', 'Uruguay', 'Croatia',\n",
    "            'Denmark', 'Iceland', 'Costa Rica', 'Sweden', 'Tunisia', 'Egypt', 'Senegal', 'Iran',\n",
    "            'Serbia', 'Nigeria', 'Australia', 'Japan', 'Morocco', 'Panama', 'Korea Republic', 'Saudi Arabia']\n",
    "for item in team_list:\n",
    "    if item not in s_score.index:\n",
    "        print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>year</th>\n",
       "      <th>diff_score</th>\n",
       "      <th>win_team</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>France</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>3</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>1930-07-18</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Peru</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>False</td>\n",
       "      <td>1930</td>\n",
       "      <td>1</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>1930-07-19</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>3</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>1930-07-30</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>False</td>\n",
       "      <td>1930</td>\n",
       "      <td>2</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  home_team  away_team  home_score  away_score      tournament  \\\n",
       "1278 1930-07-13     France     Mexico           4           1  FIFA World Cup   \n",
       "1281 1930-07-15  Argentina     France           1           0  FIFA World Cup   \n",
       "1286 1930-07-18    Uruguay       Peru           1           0  FIFA World Cup   \n",
       "1287 1930-07-19  Argentina     Mexico           6           3  FIFA World Cup   \n",
       "1296 1930-07-30    Uruguay  Argentina           4           2  FIFA World Cup   \n",
       "\n",
       "            city  country  neutral  year  diff_score   win_team     winner  \n",
       "1278  Montevideo  Uruguay     True  1930           3     France     France  \n",
       "1281  Montevideo  Uruguay     True  1930           1  Argentina  Argentina  \n",
       "1286  Montevideo  Uruguay    False  1930           1    Uruguay    Uruguay  \n",
       "1287  Montevideo  Uruguay     True  1930           3  Argentina  Argentina  \n",
       "1296  Montevideo  Uruguay    False  1930           2    Uruguay    Uruguay  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Iceland and Panama, the first time to top 32 of World Cup\n",
    "# 2. Egypt, no win of tournament in World Cup\n",
    "\n",
    "df_top32 = df_FIFA[(df_FIFA['home_team'].isin(team_list))&(df_FIFA['away_team'].isin(team_list))]\n",
    "df_top32.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s_score.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32强赢球场数情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc13e860>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1499e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_32 = df_top32.groupby('win_team')['win_team'].count()\n",
    "s_32.sort_values(ascending=False, inplace=True)\n",
    "s_32.drop(labels=['Draw'], inplace=True)\n",
    "s_32.sort_values(ascending=True,inplace=True)\n",
    "s_32.plot(kind='barh', figsize=(8,12), title='Top 32 of World Cup since year 1872')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32强进球数量情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "Germany           132\n",
       "Brazil            121\n",
       "Argentina          76\n",
       "France             57\n",
       "England            53\n",
       "Sweden             45\n",
       "Spain              45\n",
       "Uruguay            39\n",
       "Belgium            37\n",
       "Mexico             31\n",
       "Poland             30\n",
       "Russia             27\n",
       "Denmark            23\n",
       "Portugal           17\n",
       "Korea Republic     17\n",
       "Colombia           16\n",
       "Switzerland        16\n",
       "Japan              11\n",
       "Peru               11\n",
       "Nigeria            10\n",
       "Costa Rica         10\n",
       "Croatia             9\n",
       "Tunisia             7\n",
       "Australia           7\n",
       "Senegal             7\n",
       "Saudi Arabia        6\n",
       "Morocco             5\n",
       "Serbia              5\n",
       "Iran                2\n",
       "Egypt               0\n",
       "Name: score, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_home_32 = df_top32[['home_team', 'home_score']]\n",
    "column_update = ['team', 'score']\n",
    "df_score_home_32.columns = column_update\n",
    "df_score_away_32 = df_top32[['away_team', 'away_score']]\n",
    "df_score_away_32.columns = column_update\n",
    "df_score_32 = pd.concat([df_score_home_32,df_score_away_32], ignore_index=True)\n",
    "s_score_32 = df_score_32.groupby('team')['score'].sum()\n",
    "s_score_32.sort_values(ascending=False, inplace=True)\n",
    "s_score_32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc5c69b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc103f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_score_32.sort_values(ascending=True, inplace=True)\n",
    "s_score_32.plot(kind='barh', figsize=(8,12), title='Top 32 in Total Scores of World Cup since year 1872')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32强在最近10届世界杯的表现数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>year</th>\n",
       "      <th>diff_score</th>\n",
       "      <th>win_team</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10423</th>\n",
       "      <td>1978-06-01</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Poland</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>1978</td>\n",
       "      <td>0</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10426</th>\n",
       "      <td>1978-06-02</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Rosario</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>1978</td>\n",
       "      <td>-2</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10428</th>\n",
       "      <td>1978-06-03</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Mar del Plata</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>1978</td>\n",
       "      <td>0</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10431</th>\n",
       "      <td>1978-06-06</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>False</td>\n",
       "      <td>1978</td>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10432</th>\n",
       "      <td>1978-06-06</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Córdoba</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>1978</td>\n",
       "      <td>6</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  home_team away_team  home_score  away_score      tournament  \\\n",
       "10423 1978-06-01    Germany    Poland           0           0  FIFA World Cup   \n",
       "10426 1978-06-02     Mexico   Tunisia           1           3  FIFA World Cup   \n",
       "10428 1978-06-03     Brazil    Sweden           1           1  FIFA World Cup   \n",
       "10431 1978-06-06  Argentina    France           2           1  FIFA World Cup   \n",
       "10432 1978-06-06    Germany    Mexico           6           0  FIFA World Cup   \n",
       "\n",
       "                city    country  neutral  year  diff_score   win_team  \\\n",
       "10423   Buenos Aires  Argentina     True  1978           0       Draw   \n",
       "10426        Rosario  Argentina     True  1978          -2    Tunisia   \n",
       "10428  Mar del Plata  Argentina     True  1978           0       Draw   \n",
       "10431   Buenos Aires  Argentina    False  1978           1  Argentina   \n",
       "10432        Córdoba  Argentina     True  1978           6    Germany   \n",
       "\n",
       "          winner  \n",
       "10423       Draw  \n",
       "10426    Tunisia  \n",
       "10428       Draw  \n",
       "10431  Argentina  \n",
       "10432    Germany  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top32_10 = df_top32[df_top32['year']>=1978]\n",
    "df_top32_10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 32强在最近10届世界杯的赢球场数情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc626550>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6a7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_32_10 = df_top32_10.groupby('win_team')['win_team'].count()\n",
    "s_32_10.sort_values(ascending=False, inplace=True)\n",
    "s_32_10.drop(labels=['Draw'], inplace=True)\n",
    "s_32_10.sort_values(ascending=True,inplace=True)\n",
    "s_32_10.plot(kind='barh', figsize=(8,12), title='Top 32 of World Cup since year 1978')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 32强在最近10届世界杯的进球总数量情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "Germany      81\n",
       "Argentina    56\n",
       "Brazil       55\n",
       "France       37\n",
       "Spain        31\n",
       "Name: score, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_home_32_10 = df_top32_10[['home_team', 'home_score']]\n",
    "column_update = ['team', 'score']\n",
    "df_score_home_32_10.columns = column_update\n",
    "df_score_away_32_10 = df_top32_10[['away_team', 'away_score']]\n",
    "df_score_away_32_10.columns = column_update\n",
    "df_score_32_10 = pd.concat([df_score_home_32_10,df_score_away_32_10], ignore_index=True)\n",
    "s_score_32_10 = df_score_32_10.groupby('team')['score'].sum()\n",
    "s_score_32_10.sort_values(ascending=False, inplace=True)\n",
    "s_score_32_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcad1240>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6acc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_score_32_10.sort_values(ascending=True, inplace=True)\n",
    "s_score_32_10.plot(kind='barh', figsize=(8,12), title='Top 32 in Total Scores of World Cup since year 1978')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 32强在最近4届世界杯的表现数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 32强在最近4届世界杯的赢球场数情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>year</th>\n",
       "      <th>diff_score</th>\n",
       "      <th>win_team</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24528</th>\n",
       "      <td>2002-05-31</td>\n",
       "      <td>France</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Korea Republic</td>\n",
       "      <td>True</td>\n",
       "      <td>2002</td>\n",
       "      <td>-1</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Senegal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24529</th>\n",
       "      <td>2002-06-01</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Sapporo</td>\n",
       "      <td>Japan</td>\n",
       "      <td>True</td>\n",
       "      <td>2002</td>\n",
       "      <td>8</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24531</th>\n",
       "      <td>2002-06-01</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Ulsan</td>\n",
       "      <td>Korea Republic</td>\n",
       "      <td>True</td>\n",
       "      <td>2002</td>\n",
       "      <td>-1</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24532</th>\n",
       "      <td>2002-06-02</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Kashima</td>\n",
       "      <td>Japan</td>\n",
       "      <td>True</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24533</th>\n",
       "      <td>2002-06-02</td>\n",
       "      <td>England</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Saitama</td>\n",
       "      <td>Japan</td>\n",
       "      <td>True</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  home_team     away_team  home_score  away_score  \\\n",
       "24528 2002-05-31     France       Senegal           0           1   \n",
       "24529 2002-06-01    Germany  Saudi Arabia           8           0   \n",
       "24531 2002-06-01    Uruguay       Denmark           1           2   \n",
       "24532 2002-06-02  Argentina       Nigeria           1           0   \n",
       "24533 2002-06-02    England        Sweden           1           1   \n",
       "\n",
       "           tournament     city         country  neutral  year  diff_score  \\\n",
       "24528  FIFA World Cup    Seoul  Korea Republic     True  2002          -1   \n",
       "24529  FIFA World Cup  Sapporo           Japan     True  2002           8   \n",
       "24531  FIFA World Cup    Ulsan  Korea Republic     True  2002          -1   \n",
       "24532  FIFA World Cup  Kashima           Japan     True  2002           1   \n",
       "24533  FIFA World Cup  Saitama           Japan     True  2002           0   \n",
       "\n",
       "        win_team     winner  \n",
       "24528    Senegal    Senegal  \n",
       "24529    Germany    Germany  \n",
       "24531    Denmark    Denmark  \n",
       "24532  Argentina  Argentina  \n",
       "24533       Draw       Draw  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top32_2002 = df_top32[df_top32['year']>=2002]\n",
    "df_top32_2002.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcd8c5c0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcda0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_32_2002 = df_top32_2002.groupby('win_team')['win_team'].count()\n",
    "s_32_2002.sort_values(ascending=False, inplace=True)\n",
    "s_32_2002.drop(labels=['Draw'], inplace=True)\n",
    "s_32_2002.sort_values(ascending=True,inplace=True)\n",
    "s_32_2002.plot(kind='barh', figsize=(8,12), title='Top 32 of World Cup since year 2002')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 32强在最近4届世界杯的进球总数量情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "Germany      48\n",
       "Argentina    25\n",
       "Brazil       24\n",
       "France       13\n",
       "Mexico       12\n",
       "Name: score, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_home_32_2002 = df_top32_2002[['home_team', 'home_score']]\n",
    "column_update = ['team', 'score']\n",
    "df_score_home_32_2002.columns = column_update\n",
    "df_score_away_32_2002 = df_top32_2002[['away_team', 'away_score']]\n",
    "df_score_away_32_2002.columns = column_update\n",
    "df_score_32_2002 = pd.concat([df_score_home_32_2002,df_score_away_32_2002], ignore_index=True)\n",
    "s_score_32_2002 = df_score_32_2002.groupby('team')['score'].sum()\n",
    "s_score_32_2002.sort_values(ascending=False, inplace=True)\n",
    "s_score_32_2002.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc616048>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdbea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_score_32_2002.sort_values(ascending=True, inplace=True)\n",
    "s_score_32_2002.plot(kind='barh', figsize=(8,12), title='Top 32 in Total Scores of World Cup since year 2002')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
